Real-time provisioning of directed digital content based on decomposed structured messaging data

ABSTRACT

The disclosed embodiments include computer-implemented systems and processes that generate and provision, in real time, directed digital content based on decomposed structured messaging data. For example, an apparatus may receive a plurality of messages that characterize first data exchanges initiated between a first counterparty and second counterparties during a first temporal interval. Each of the messages includes elements of message data associated with a real-time payment requested from the first counterparty by a corresponding one of the second counterparties. Based on the elements of message data, that apparatus may predict an occurrence of a second exchange of data that involves the first counterparty during a second temporal interval, and may transmit notification data that includes product data characterizing an available product associated with the predicted occurrence of the second data exchange to a device operable by the first counterparty for presentation within a digital interface.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 63/194,747, filed on May 28, 2021, the entiredisclosure of which is expressly incorporated herein by reference to itsentirety.

TECHNICAL FIELD

The disclosed embodiments generally relate to computer-implementedsystems and processes that generate and provision, in real time,directed digital content based on decomposed structured messaging data.

BACKGROUND

The mass adoption of smart phones and digital payments within the globalmarketplace drives an increasingly rapid adoption of real-time payment(RTP) technologies by financial institutions, consumers, vendors andmerchants, and other participants in the payment ecosystem. RTPtechnologies often emphasize data, messaging, and globalinteroperability and in contrast to many payment rails, such as thosethat support credit card payments, embrace the near ubiquity of mobiletechnologies in daily life to provide, to the participants in the RTPecosystem, real-time service and access to funds.

SUMMARY

In some examples, an apparatus includes a communications interface, amemory storing instructions, and at least one processor coupled to thecommunications interface and to the memory. The at least one processoris configured to execute the instructions to receive a plurality ofmessages via the communications interface. Each of the messages isassociated with a real-time payment requested from a first counterpartyby a corresponding second counterparty. Each of the messages includeselements of message data that are disposed within corresponding messagefields, and that characterize a first exchange of data initiated betweenthe first counterparty and the corresponding second counterparty duringa first temporal interval. The at least one processor is configured toexecute the instructions to, based on the elements of message data,perform operations that predict an occurrence of a second exchange ofdata that involves the first counterparty during a second temporalinterval. The second temporal interval is disposed subsequent to thefirst temporal interval. The at least one processor is configured toexecute the instructions to generate product data characterizing aproduct that is available to the first counterparty and associated withthe predicted occurrence of the second data exchange, and transmit, viathe communications interface, notification data that includes theproduct data to a device operable by the first counterparty. Thenotification data causes an application program executed at the deviceto present a portion of the product data within a digital interface.

In other examples, a computer-implemented method includes receiving aplurality of messages using at least one processor. Each of the messagesis associated with a real-time payment requested from a firstcounterparty by a corresponding second counterparty. Each of themessages includes elements of message data that are disposed withincorresponding message fields, and that characterize a first exchange ofdata initiated between the first counterparty and the correspondingsecond counterparty during a first temporal interval. Thecomputer-implemented method includes, based on the elements of messagedata, performing operations, using the at least one processor, thatpredict an occurrence of a second exchange of data that involves thefirst counterparty during a second temporal interval. The secondtemporal interval is disposed subsequent to the first temporal interval.The computer-implemented method includes generating, using the at leastone processor, product data characterizing a product that is availableto the first counterparty and associated with the predicted occurrenceof the second data exchange, and transmitting, using the at least oneprocessor, notification data that includes the product data to a deviceoperable by the first counterparty. The notification data causes anapplication program executed at the device to present a portion of theproduct data within a digital interface.

Further, in some examples, a tangible, non-transitory computer-readablemedium stores instructions that, when executed by at least oneprocessor, cause the at least one processor to perform a method thatincludes receiving a plurality of messages. Each of the messages isassociated with a real-time payment requested from a first counterpartyby a corresponding second counterparty. Each of the messages includeselements of message data that are disposed within corresponding messagefields, and that characterize a first exchange of data initiated betweenthe first counterparty and the corresponding second counterparty duringa first temporal interval. The method includes, based on the elements ofmessage data, performing operations that predict an occurrence of asecond exchange of data that involves the first counterparty during asecond temporal interval. The second temporal interval is disposedsubsequent to the first temporal interval. The method includesgenerating product data characterizing a product that is available tothe first counterparty and associated with the predicted occurrence ofthe second data exchange, and transmitting notification data thatincludes the product data to a device operable by the firstcounterparty. The notification data causes an application programexecuted at the device to present a portion of the product data within adigital interface.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed. Further, theaccompanying drawings, which are incorporated in and constitute a partof this specification, illustrate aspects of the present disclosure andtogether with the description, serve to explain principles of thedisclosed embodiments as set forth in the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary computing environment, inaccordance with some exemplary embodiments.

FIG. 2A is a block diagram illustrating a portion of an exemplarycomputing environment, in accordance with some exemplary embodiments.

FIG. 2B illustrates portions of an exemplary request for payment (RFP)message, in accordance with some exemplary embodiments.

FIGS. 3A, 3B, and 3C are block diagrams illustrating portions of anexemplary computing environment, in accordance with some exemplaryembodiments.

FIGS. 4A, 4B, and 4C are flowcharts of exemplary processes forprovisioning directed product data to customer devices based onstructured messaging data, in accordance with some exemplaryembodiments.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Today, the mass adoption of smart phones and digital payments within theglobal marketplace drives an adoption of real-time payment (RTP)technologies by financial institutions, consumers, vendors andmerchants, and other participants in the payment ecosystem. These RTPtechnologies often emphasize data, messaging, and globalinteroperability and in contrast to conventional payment rails, mayembrace the near ubiquity of mobile technologies in daily life toprovide, to the participants in the RTP ecosystem, real-time service andaccess to funds. To facilitate the strong emphasis on data, messaging,and global interoperability between financial institutions, many RTPtechnologies adopt, and exchange data formatted in accordance with, oneor more standardized data-exchange protocols, such as the ISO 20022standard for electronic data exchange between financial institutions.

For example, a customer of a financial institution may initiate aplurality of transactions to purchase one or more products or servicesfrom corresponding merchants or retailers, either through in-personinteraction at a physical location of the merchant or retailer, orthrough digital interactions with a computing system of the merchant(e.g., via a web page or other digital portal). In some instances, andto fund the initiated purchase transaction, the customer may provideeach of the merchants with data characterizing a payment instrument,such as credit card account issued by the financial institution (e.g.,via input provisioned to the web page or digital portal, or based on aninterrogation of a physical payment card by point-of-sale terminal,etc.). The merchant computing systems may perform operations thatgenerate elements of messaging data that identify and characterize thecorresponding merchant and the corresponding initiated purchasetransaction, and that include portions of the data characterizing thepayment instrument, and that submit the generated elements of messagingdata to a transaction processing network or payment rail in accordancewith a predetermined schedule (e.g., in batch form with other elementsof messaging data at a predetermined time on a daily basis). In someinstances, one or more computing systems of the transaction processingnetwork or payment rail may perform operations that execute, clear, andsettle each of the initiated purchase transactions involving thecorresponding payment instrument within a predetermined temporalinterval subsequent to the initiation of the purchase transaction, suchas, but not limited to, forty-eight hours.

In other examples, the merchants (or the financial institutions of themerchants) and the financial institution of the customer may representparticipants in the RTP ecosystem, and the merchant computing systems(or a computing system associated with the financial institution of oneor more of the merchants) may generate a corresponding message (e.g., aRequest for Payment (RFP) message) that requests a real-time paymentfrom the customer that funds the corresponding initiated purchasetransaction, and may transmit that message to one or more computingsystems of the financial institution of the customer (e.g., directly orthrough one or more intermediate systems associated with the RTPecosystem, such as a clearinghouse system). Each of the generated andtransmitted RFP message may, for example, be formatted in accordancewith the ISO 20022 data-exchange format, and may include message fieldspopulated with information that includes, but is not limited to,information identifying the customer and the corresponding merchant,information characterizing the corresponding requested payment (e.g., arequested payment amount, a requested payment date, an identifier of anaccount selected by the customer to fund the requested, real-timepayment, or an identifier of an account of the merchant capable ofreceiving the requested, real-time payment, etc.), and informationcharacterizing the corresponding initiated purchase transaction (e.g., atransaction date or time, or an identifier of one or more of theproducts or services involved in the initiated purchase transaction,such as a corresponding UPC, etc.). Further, the ISO-20022-compliant RFPmessage may also include a link within a structured or unstructuredmessage field to information, such as remittance data, associated withthe requested, real-time payment (e.g., a long- or shortened UniformResource Location (URL) pointing to a formatted invoice or statementthat includes any of the information described herein).

In some examples, the elements of structured or unstructured datamaintained within the message fields of exemplary, ISO-20022-compliantRFP messages described herein may extend beyond the often-limitedcontent of the message data transmitted across many existing paymentrails and transaction processing networks. Further, when intercepted anddecomposed by a computing system of the financial institution of thecustomer, these elements of structured or unstructured RFP message datamay be processed by the computing system of the financial institution toadaptively determine the terms and conditions of a financial productthat is “pre-approved” for provisioning to the customer by the financialinstitution, and that is available and fund not only real-time paymentsassociated with purchase transactions initiated by the customer atcorresponding ones of the merchants (and requested by corresponding onesof the merchants) during a prior temporal interval, but also predictedoccurrences of one or additional purchase transactions during a futuretemporal interval that are consistent with a intent or purpose of thepurchase transactions initiated during the prior temporal interval. Byway of example, and using any of the exemplary processes describedherein, the computing system of the financial institution may provision,to a device operable by the customer, a product notification thatidentifies and characterizes the pre-approved financial product, such asa secured credit product or a loan product, and the terms and conditionsassociated with that available financial product, and an applicationprogram executed by the customer device may perform operations,described herein, that render the product notification for presentationwith a portion of a digital interface.

Upon presentation within the digital interface of the customer device,the product notification may, among other things, identify thepre-approved financial product and the determined terms and conditions,and may prompt the customer to accept, or alternatively decline, theoffer to fund the requested, real-time payments associated with theinitiated purchase transaction (e.g., associated with corresponding onesof the received or intercepted RFP messages) using the availablefinancial product in accordance with the determine terms and conditions.Further, and based on confirmation data indicative of the customeracceptance of the offered financial product, the computing system of thefinancial institution may perform any of the exemplary processesdescribed herein to issue the now-accepted financial product to thecustomer, and to generate additional, ISO-2002-compliant RTP messagesthat, when provisioned to one or more computing systems of the merchantsassociated with the initiated purchase transactions and the received orintercepted RFP messages (or to an intermediate computing system, suchas a computing system of the merchants' financial institutions),provides the corresponding requested payment using funds drawn from theissued financial product in real-time and contemporaneously with theinitiation of the purchase transactions and the real-time paymentsrequested by corresponding ones of the merchants.

Certain of the exemplary processes described herein, which decompose thestructured message fields of a plurality of ISO-20022-compliant RFPmessages received during a prior temporal interval to obtaincorresponding elements of decomposed message data characterizing thecustomer, the merchant, and the initiated purchase transaction, and therequested, real-time payment, which analyze the elements of decomposemessage data to determine terms and conditions of a financial productappropriate to, and available to fund not each of the purchasetransactions initiated during the prior temporal interval, but alsopredicted occurrences of one or more additional purchase transactionsduring a future temporal interval, which provision data characterizingthe available financial product to the customer device for presentationwithin a digital interface in real-time and contemporaneously with theinitiated purchase transactions and prior to an initiation ofadditional, future purchase transactions, may be implemented in additionto, or as an alternate to, many processes that rely on the often-limitedcontent of temporally delayed message data transmitted across manyexisting payment rails and transaction processing networks.

A. Exemplary Computing Environments

FIG. 1 is a diagram illustrating an exemplary computing environment 100that includes, among other things, one or more computing devices, suchas a client device 102, and one or more computing systems, such one ormore merchant computing systems (including merchant systems 110, 114,and 118), and a financial institution (FI) system 130, each of which maybe operatively connected to, and interconnected across, one or morecommunications networks, such as communications network 120. Examples ofcommunications network 120 include, but are not limited to, a wirelesslocal area network (LAN), e.g., a “Wi-Fi” network, a network utilizingradio-frequency (RF) communication protocols, a Near Field Communication(NFC) network, a wireless Metropolitan Area Network (MAN) connectingmultiple wireless LANs, and a wide area network (WAN), e.g., theInternet.

Client device 102 may include a computing device having one or moretangible, non-transitory memories, such as memory 105, that store dataand/or software instructions, and one or more processors, e.g.,processor 104, configured to execute the software instructions. The oneor more tangible, non-transitory memories may, in some aspects, storesoftware applications, application modules, and other elements of codeexecutable by the one or more processors, such as, but not limited to,an executable web browser (e.g., Google Chrome™, Apple Safari™, etc.),an executable application associated with one of merchant systems 110,114, or 118 (e.g., merchant application 106), and additionally oralternatively, an executable application associated with FI computingsystem 130 (e.g., mobile banking application 108).

In some instances, not illustrated in FIG. 1 , memory 105 may alsoinclude one or more structured or unstructured data repositories ordatabases, and client device 102 may maintain one or more elements ofdevice data and location data within the one or more structured orunstructured data repositories or databases. For example, the elementsof device data may uniquely identify client device 102 within computingenvironment 100, and may include, but are not limited to, an InternetProtocol (IP) address assigned to client device 102 or a media accesscontrol (MAC) layer assigned to client device 102.

Client device 102 may also include a display unit 109A configured topresent interface elements to a corresponding user, such as a user 101,and an input unit 109B configured to receive input from user 101, e.g.,in response to the interface elements presented through display unit109A. By way of example, display unit 109A may include, but is notlimited to, an LCD display unit or other appropriate type of displayunit, and input unit 109B may include, but is not limited to, a keypad,keyboard, touchscreen, voice activated control technologies, orappropriate type of input unit. Further, in additional aspects (notillustrated in FIG. 1 ), the functionalities of display unit 109A andinput unit 109B may be combined into a single device, e.g., apressure-sensitive touchscreen display unit that presents interfaceelements and receives input from user 101. Client device 102 may alsoinclude a communications interface 109C, such as a wireless transceiverdevice, coupled to processor 104 and configured by processor 104 toestablish and maintain communications with communications network 120via one or more communication protocols, such as WiFi®, Bluetooth®, NFC,a cellular communications protocol (e.g., LTE®, CDMA®, GSM®, etc.), orany other suitable communications protocol.

Examples of client device 102 may include, but are not limited to, apersonal computer, a laptop computer, a tablet computer, a notebookcomputer, a hand-held computer, a personal digital assistant, a portablenavigation device, a mobile phone, a smart phone, a wearable computingdevice (e.g., a smart watch, a wearable activity monitor, wearable smartjewelry, and glasses and other optical devices that include opticalhead-mounted displays (OHMDs)), an embedded computing device (e.g., incommunication with a smart textile or electronic fabric), and any othertype of computing device that may be configured to store data andsoftware instructions, execute software instructions to performoperations, and/or display information on an interface device or unit,such as display unit 109A. In some instances, client device 102 may alsoestablish communications with one or more additional computing systemsor devices operating within environment 100 across a wired or wirelesscommunications channel, e.g., via the communications interface 109Cusing any appropriate communications protocol. Further, user 101 mayoperate client device 102 and may do so to cause client device 102 toperform one or more exemplary processes described herein.

Each of the one or more merchant computing systems, including merchantsystem 110, merchant system 114, and merchant system 118, and FIcomputing system 130 may represent a computing system that includes oneor more servers and one or more tangible, non-transitory memory devicesstoring executable code, application engines, or application modules.Each of the one or more servers may include one or more processors,which may be configured to execute portions of the stored code,application engines, or application modules to perform operationsconsistent with the disclosed exemplary embodiments. For example, asillustrated in FIG. 1 , the one or more servers of FI computing system130 may include server 132 having one or more processors configured toexecute portions of the stored code, application engines, or applicationmodules maintained within the one or more corresponding, tangible,non-transitory memories. In some instances, each of the merchantcomputing systems, including merchant systems 110, 114, and 118, and/orFI computing system 130 may correspond to a discrete computing system,although in other instances, one or more of merchant systems 110, 114,and 118, or FI computing system 130, may correspond to a distributedcomputing system having multiple, computing components distributedacross an appropriate computing network, such as communications network120 of FIG. 1 , or those established and maintained by one or morecloud-based providers, such as Microsoft Azure™, Amazon Web Services™,or another third-party, cloud-services provider. Further, each of themerchant computing systems, including merchant systems 110, 114, and118, and FI computing system 130 may also include one or morecommunications units, devices, or interfaces, such as one or morewireless transceivers, coupled to the one or more processors foraccommodating wired or wireless internet communication across network120 with other computing systems and devices operating withinenvironment 100 (not illustrated in FIG. 1 ).

By way of example, each of merchant systems 110, 114, and 118 may beassociated with, or operated by, a corresponding merchant that offersproducts or services for sale to one or more customers, such as, but notlimited to, user 101 that operates client device 102. In some instances,described herein, merchant system 110 may be associated with, oroperated by, a first retailer (e.g., “Sam's Haberdashery”), merchantsystem 114 may be associated with, or operated by, a second retailer(e.g., “Woody's Suits”), and merchant system 118 may be associated with,or operated by, a third retailer (e.g., “Claude's Clothes”). Further,one or more of merchant systems 110, 114, and 118 may exchange dataprogrammatically with one or more application programs executed atclient device 102, such as merchant application 106, and based on theprogrammatically exchanged data, client device 102 may perform any ofthe exemplary processes described herein to initiate a transaction topurchase one or more of the products or services offered for sale bymerchant 111.

Further, and as described herein, FI computing system 130 may beassociated with, or operated by, a financial institution that offersfinancial products or services to one or more customers, such as, butnot limited to, user 101. The financial products or services may, forexample, include a financial product issued to user 101 by the financialinstitution and available to fund the initiated purchase transaction,and examples of the payment instrument may include, but are not limitedto, a credit card account issued by the financial institution, a securedor unsecured credit product issued by the financial institution (e.g.,an unsecured or secured line-of-credit, an unsecured personal loan,etc.), or a checking, savings, or other deposit account issued by andmaintained at the financial institution.

In some instances, FI computing system 130 may perform any of theexemplary processes described herein to obtain, receive, or intercept aplurality of request-for-payment (RFP) message associated with purchasetransactions initiated by a first counterparty (e.g., user 101 of FIG. 1) and involving corresponding second counterparties (e.g., one of afirst retailer, a second retailer, or a third retailer associated withmerchant systems 110, 114, or 118 of FIG. 1 ). As described herein, thereceived RFP message may be formatted and structured in accordance withone or more standardized data-exchange protocols, such as the ISO 20022standard for electronic data exchange between financial institutions.Further, and based on elements of mapping data that characterize astructure, composition, or format of one or more data fields of theISO-20022-compliant RFP message, FI computing system 130 may perform anyof the exemplary processes described herein to decompose each of thereceived RFP message and obtain data characterizing user 101, acorresponding one of the merchants (e.g., the first retailer, the secondretailer, or the third retailer, as described herein), and additionally,or alternatively, the initiated purchase transaction.

For example, the obtained data (e.g., “decomposed field” data) mayinclude one or more of: (i) customer data identifying user 101, such asa unique customer identifier (e.g., a customer name, an alphanumericlogin credential, etc.) and a postal address; (ii) payment datacharacterizing the real-time payment transaction, such as a transactionamount, a requested transaction date or time, an identifier of a productor service involved in the transaction, and an identifier of a customeraccount (e.g., from which the transaction amount will be debited) and amerchant account (e.g., to which the transaction amount will becredited); (iii) counterparty data identifying the correspondingmerchant, such as a counterparty name (e.g., a merchant name, etc.) andpostal address; and (iv) transaction data that identifies a value of oneor more parameters of corresponding ones of the initiated purchasetransactions (e.g., a transaction amount, a transaction date or time, anidentifier of one or more of the products or services involved incorresponding ones of the initiated purchase transactions).

FI computing system 130 may also perform any of the exemplary processesdescribed herein to analyze the elements of decomposed elements ofcustomer, counterparty, transaction, or payment data obtained from themessage fields of each of the RFP messages, which characterize purchasetransactions initiated by a counterparty (e.g., user 101) during acorresponding, prior temporal interval (e.g., the decomposed elements ofcustomer, counterparty, merchant, transaction, or payment data describedherein). Based on the analysis of the decomposed elements of customer,counterparty, merchant, transaction, or payment, FI computing system 130may perform any of the exemplary processes described herein to determinea likelihood that user 101 will initiate one or more additional purchasetransactions during a future temporal interval, and further, to predicta value of one or more parameters that characterize these additionalpurchase transactions during the future temporal interval, such as, butnot limited to a transaction amount, a corresponding counterparty (e.g.,a corresponding merchant), and an identifier of one or more products orservices involved in corresponding ones of the additional purchasetransactions during the future temporal interval.

FI computing system 130 may also perform any of the exemplary processesdescribed processes described herein to establish that a credit or loanproduct, such as an unsecured installment loan or an unsecured personalloan, is available for provisioning to user 101 and available to fundboth the purchase transactions initiated during the prior temporalinterval and the predicted occurrences of the additional purchasetransactions during the future temporal interval. In some instances,based on an application of one or more internal qualification orunderwriting criteria to data characterizing user 101, and interactionsbetween user 101 and the financial institution or one or more unrelatedfinancial institution, and a use, or misuse, of financial productprovisioned by the financial institution of the unrelated financialinstitutions, FI computing system 130 may perform any of the exemplaryprocesses described herein to “pre-approve” user 101 for the credit orloan product in an amount sufficient to fund both the initiated purchasetransactions and the predicted occurrences of the future purchasetransactions (and in some instances, season variations in purchasing orspending habits of the user 101). Further, FI computing system 130 mayperform operations that generate a product notification characterizingthe pre-approved credit or loan product and one or more determined termsand conditions, and provision the product notification to a deviceoperable by user 101, such as client device 102, in real-time andcontemporaneously with an interception of receipt of each of pluralityof RFP messages and in some instances, prior to an execution of arequested, real-time payment associated with at least one of the RFPmessages, e.g., while user 101 continues to shop for products orservices and continues to initiate purchase transactions.

To facilitate a performance of one or more of these exemplary processes,FI computing system 130 may maintain, within the one or more tangible,non-transitory memories, a data repository 134 that includes, but is notlimited to, a request-for-payment (RFP) queue 135, a candidate financialproduct data store 136, a mapping data store 138, a customer data store140, an incentive data store 142, and a real-time payment (RTP) datastore 144. RFP queue 135 may include one or more discrete RFP messagesreceived by FI computing system 130 using any of the exemplary processesdescribed herein. In some instances, the RFP messages maintained withinRFP queue 135 may be prioritized in accordance with a time or date ofreceipt by FI computing system 130 or with requested payment dataassociated with each of the RFP messages, and each of the prioritizedRFP messages may be associated with a corresponding temporal pendency.Further, FI computing system 130 may perform any of the exemplaryprocesses described herein to provision elements of notification dataassociated with each of the RFP messages to a computing system or deviceassociated with a corresponding customer (e.g., client device 102associated with user 101), and FI computing system 130 may performoperations that maintain each of the RFP messages within RFP queue 135until a receipt, at FI computing system 130, of confirmation data fromcorresponding ones of the computing systems or devices indicating anapproval, or a rejection, of the corresponding requested payment, oruntil an expiration of the corresponding pendency period.

Candidate financial product data store 136 may include structured orunstructured data that characterizes one or more candidate financialproducts, such as, but not limited to, one or more the exemplary,secured credit or loan products described herein, that a customer, suchas user 101, may select to fund one or more of the requested, real-timepayments associated with corresponding ones of the intercepted orreceived RFP message. In some instances, the elements of candidatefinancial product data store 136 may include, for each of the candidatefinancial products, a unique product identifier (e.g., a product name,etc.), data characterize terms and conditions for each candidatefinancial product, and further, data characterizing internalqualification or underwriting procedures for each candidate financialproduct, as described herein.

Mapping data store 138 may include structured or unstructured datarecords that maintain one or more elements of field mapping data 138A.For example, and as described herein, FI computing system 130 mayreceive, obtain, or intercept one or more RFP messages, each of whichmay be formatted and structured in accordance with a corresponding,standardized data-exchange protocol, such as the ISO 20022 standard forelectronic data exchange between financial institutions. In someinstances, the one or more elements of field mapping data 138A maycharacterize a structure, composition, or format of the message datapopulating one or more data fields of the ISO-20022-compliant RFPmessage, or a corresponding RFP message compliant with an additional, oralternate, standardized data-exchange protocol.

In some instances, customer data store 140 may include structured orunstructured data records that maintain information identifying andcharacterizing one or more customers of the financial institution, andfurther, interactions between these customers and not only the financialinstitution, but also other unrelated third parties, such as themerchants or retailers described herein. For example, as illustrated inFIG. 1 , customer data store 140 may include one or more elements ofcustomer profile data 140A, which identify and characterizecorresponding ones of the customers of the financial institution, one ormore elements of account data 140B, which may identify and characterizeone or more accounts held by the customers, one or more elements oftransaction data 140C, which identify and characterize prior purchase orpayment transactions involving the customers of the financialinstitution (such as, but not limited to, user 101), and one or moreelements of third-party data 140D associated with correspondingcustomers of the financial institution.

By way of example, for a corresponding one of the customer, such as user101, the elements of customer profile data 140A may include, but are notlimited to, a customer identifier of user 101 (e.g., an alphanumericlogin credential, a customer name, etc.), a postal address of user 101,and values of one or more demographic parameters characterizing user 101(e.g., a customer age, customer profession, etc.). The accounts held bythe customers of the financial institution may include, but are notlimited to, a deposit account (e.g., a checking or a savings accountissued by the financial institution), a credit-card account, or anaccount associated with an additional, or alternate, financial product,such as an unsecured personal loan or an installment loan, and theelements of account data 140B may include, for an account held by acorresponding one of the customers, such as user 101, the customeridentifier of user 101, all or a portion of an account number (e.g., anactual account number, a tokenized account number, etc.), and datacharacterizing a status of the account (e.g., a current balance, anoverdue balance, length of account existence, etc.) and interactionsbetween user 101 and the account (e.g., amounts and dates ofwithdrawals, etc.). Further, the elements of transaction data 140C mayinclude the customer identifier of user 101, data identifying one ormore prior purchase or payment transactions initiated by user 101 (e.g.,a unique, alphanumeric transaction identifier assigned by FI computingsystem 130), and may include values of transaction parameters thatcharacterize each of the prior purchase or payment transactions, such asa transaction data or time, a transaction amount, an identifier of acorresponding counterparty, or an identifier of an account (e.g., anaccount number, etc.) that funds, or receives proceeds from, the priorpurchase or payment transaction.

The elements of third-party data 140D may, for a corresponding one ofthe customers, such as user 101, include the customer identifier of user101 and one or more elements of governmental, judicial, regulatory, orreporting data associated with, and characterizing user 101. By way ofexample, the elements of third-party data 140D associated with user 101may include one or more elements of data generated and maintained by agovernmental entity (e.g., governmental data) that identifies parcels ofreal estate, vehicles, or other tangible properties held or owned byuser 101. Additionally, or alternatively, the elements of third-partydata 140D associated with user 101 may include one or more elements ofdata generated and maintained by a reporting entity, such ascredit-bureau data that includes a credit score or data characterizingone or more credit inquiries associated with user 101 duringcorresponding temporal intervals. In some examples, FI computing system130 perform operations that receive, via a secure programmatic channelof communications, one or more of the customer-specific elements ofthird-party data 140D maintained within customer data store 140 from oneor more computing systems associated with corresponding governmental,judicial, regulatory, or reporting entities in accordance with apredetermined temporal schedule, on a continuous, streaming basis, or inresponse to a requested generated and transmitted by FI computing system130.

Incentive data store 142 may include structured or unstructured datarecords that include elements of digital content that, when presented touser 101 by client device 102 within a corresponding digital interface,identify and provide an offer or incentive for user 101 to accept apre-approved financial product, or to apply for a financial product,such as the exemplary financial products characterized by the datarecords of candidate financial product data store 136. In someinstances, the structured or unstructured data records of incentive datastore 142 may store each of the elements of digital content inconjunction with one or more elements of metadata that, among otherthings, identify a corresponding financial product (e.g., a productname, an alphanumeric product identifier assigned by the financialinstitution, etc.). Further, in some instances, the structured orunstructured data records of incentive data store 142 may store each ofthe elements of digital content in conjunction with one or more elementsof layout data, which specify a disposition of the elements of digitalcontent, or visual characteristics of the elements of digital content,when rendered for presentation within a corresponding digital interfaceby one or more application programs executed by client device 102.

RTP data store 144 may include one or more elements of decomposed fielddata generated through a decomposition of corresponding ones of thereceived RFP messages, e.g., based on the elements of field mapping data138A and through an implementation of any of the exemplary processesdescribed herein. In some instances, the elements of decomposed fielddata maintained within RTP data store 144 may establish a time-evolvingrecord of real-time payment transactions initiated by, or involving, theuser 101 and other customers of the financial institution during acurrent temporal interval and across one or more prior temporalintervals, and across various merchant classifications or geographicregions.

Further, and to facilitate the performance of any of the exemplaryprocesses described herein, FI computing system 130 may also maintain,within the one or more tangible, non-transitory memories, an applicationrepository 145 that maintains, but is not limited to, a decompositionengine 146, an analytical engine 148, and a notification engine 150,each of which may be executed by the one or more processors of server132.

For example, and upon execution by the one or more processors of FIcomputing system 130, executed decomposition engine 146 may perform anyof the exemplary processes described herein to obtain field mapping data138A from mapping data store 138, to apply field mapping data 138A to areceived, obtained, or intercepted RFP message, and based on theapplication of field mapping data 138A to the RFP message, to decomposethe RFP message and obtain elements of message data that not onlyidentify and characterize each counterparty involved in an initiatedpurchase transaction (e.g., user 101 and a corresponding merchant, asdescribed herein), but that also characterize the initiated purchasetransaction.

Further, and upon execution by the one or more processors of FIcomputing system 130, executed analytical engine 148 may perform any ofthe exemplary processes described herein to analyze the elements ofmessage data obtained from the message fields of each of the RFPmessages, which characterize purchase transactions initiated by acounterparty (e.g., user 101) during a corresponding, prior temporalinterval (e.g., the decomposed elements of customer, counterparty,merchant, transaction, or payment data described herein). Based on theanalysis of the elements of message data, executed analytical engine 148may perform any of the exemplary processes described herein to determinea likelihood that user 101 will initiate one or more additional purchasetransactions during a future temporal interval, and further, to predicta value of one or more parameters that characterize these additionalpurchase transactions during the future temporal interval, such as, butnot limited to a transaction amount, a corresponding counterparty (e.g.,a corresponding merchant), and an identifier of one or more products orservices involved in corresponding ones of the additional purchasetransactions during the future temporal interval. In some instances, thepredicted occurrences of the additional purchase transactions during thefuture temporal interval may be consistent with a determined intent orcustomer purpose that characterizes the purchase transactions initiatedby user 101 during a corresponding, prior temporal interval.

Executed analytical engine 148 may also perform any of the exemplaryprocesses described processes described herein to establish that acredit or loan product, such as an unsecured installment loan or anunsecured personal loan, is available for provisioning to user 101 andavailable to fund both the purchase transactions initiated during theprior temporal interval and the predicted occurrences of the additionalpurchase transactions during the future temporal interval. In someinstances, based on an application of one or more internal qualificationor underwriting criteria to data characterizing user 101, andinteractions between user 101 and the financial institution or one ormore unrelated financial institution, and a use, or misuse, of financialproduct provisioned by the financial institution of the unrelatedfinancial institutions, executed analytical engine 148 may perform anyof the exemplary processes described herein to “pre-approve” user 101for the credit or loan product in an amount sufficient to fund both theinitiated purchase transactions and the predicted occurrences of thefuture purchase transactions (and in some instances, season variationsin purchasing or spending habits of the user 101).

Upon execution by the one or more processors of FI computing system 130,notification engine 150 may perform any of the exemplary processesdescribed herein to generate one or more elements of notification datathat include one or more of the information identifying the availableand pre-approved credit or loan product, the corresponding loan amount,and one or more determined terms and conditions. In some instances, whenprovisioned to client device 102 by FI computing system 130, theelements of notification data may cause one or more application programsexecuted by client device 102 (e.g., mobile banking application 108) topresent interface elements within a corresponding digital interfacethat, among other things, offer the available and pre-approved credit orloan product to user 101, and that prompt user 101 to accept the offeredcredit or loan product in accordance with the determined terms andconditions. As described herein, when provisioned to user 101, theaccepted credit or loan product may fund not only the initiated purchasetransactions associated with the received or intercepted RFP messages(e.g., contemporaneously with the initiation of the purchasetransactions and the reception or interception of the RFP messages), butalso the predicted occurrences of one or more of the additional purchasetransactions during the future temporal interval.

B. Real-Time Generation and Provisioning of Directed Digital ContentBased on Decomposed Structured Messaging Data

Referring to FIG. 2A, a computing system associated with the financialinstitution, such as the FI computing system 130, may receive orintercept a plurality of RFP messages identifying and characterizingreal-time payments requested customers of the financial institution,such as, but not limited to, RFP messages 202A, 202B, and 202C. In someinstances, each of RFP message 202A, 202B, and 202C may identify andcharacterize a real-time payment requested from a customer of thefinancial institution, such as user 101, by a corresponding merchant foran initiated purchase transaction involving corresponding products orservices, and FI computing system 130 may receive each of RFP message202A, 202B, and 202C from a corresponding merchant computing system,such a corresponding one of merchant systems 110, 114, and 118 of FIG. 1, or from one or more intermediate computing system associated with theRTP ecosystem, such as, but not limited to, a computing system of aclearinghouse. As described herein, each of RFP messages 202A, 202B, and202C may be structured in accordance with the ISO 20022 standard forelectronic data exchange between financial institutions, and in someexamples, each of RFP messages 202A, 202B, and 202C may correspond to apain.013 message as specified within the ISO 20022 standard.

By way of example, user 101 may decide to transition to a new positionat a current place of employment in Washington, D.C., and inanticipation of that new position, user 101 may elect to purchaseseveral items of apparel from one or more local merchants. For instance,user 101 may initiate a first transaction at 10:30 a.m. on May 30, 2022,that purchases a shirt for $50.00 from a first merchant (such as “Sam'sHaberdashery”) located at 3262 M St N.W. in Washington, D.C., and mayinitiate a second transaction at 11:15 a.m. on May 30, 2022, thatpurchases $75.00 in pants from a second merchant (e.g., “Woody's Suits”)located at 3320 Cady's Alley N.W. in Washington, D.C. Further, user 101may also initiate at third transaction at 12:30 p.m. on May 30, 2022,that purchases a $175.00 jacket from a third retailer (e.g., “Claude'sClothing”) located at 3077 M St N.W. in Washington, D.C. In someinstances, rather than processing payment for each of the initiatedfirst, second, and third purchase transactions using a conventionalpayment processing networks (e.g., a payment rail associated with adebit- or credit-card account issued by the financial institution), oneor more computing systems operated by each of the first, second, andthird retailers (e.g., a respective one of merchant systems 110, 114,and 118 of FIG. 1 ), or one more computing systems associated with afinancial institution of each of the first, second, and third retailers,may generate a corresponding one of RFP messages 202A, 202B, and 202Cthat requests a real-time payment from user for the purchased productsassociated with respective ones of the first, second, and third purchasetransactions (e.g., a respective one of the $50.00 purchase of theshirt, the $75.00 purchase of patents, and the $175.00 purchase of thejacket), and transmit the corresponding one of RFP messages 202A, 202B,and 202C across a communications network to FI computing system 130,either directly or through one or more intermediate computing systems,such as a clearinghouse.

A programmatic interface established and maintained by FI computingsystem 130, such as application programming interface (API) 208, mayreceive each of RFP messages 202A, 202B, and 202C, and may route RFPmessage 202A, 202B, and 202C to a decomposition engine 146 executed bythe one or more processors of FI computing system 130. In some examples,FI computing system 130 may receive one or more of RFP messages 202A,202B, and 202C directly across communications network 120 via a channelof communications established programmatically between API 203 and anexecuted RTP engine of a corresponding one of merchant systems 110, 114,and 118. Further, in some examples, one or more portions of RFP messages202A, 202B, and/or 202C may be encrypted (e.g., using a publiccryptographic key associated with FI computing system 130), and executeddecomposition engine 146 may perform operations that access acorresponding decryption key maintaining within the one or moretangible, non-transitory memories of FI computing system 130 (e.g., aprivate cryptographic key associated with FI computing system 130), andthat decrypt the encrypted portions of RFP message 202A, 202B, and/or202C using the corresponding decryption key.

In some instances, executed decomposition engine 146 may store each ofRFP messages 202A, 202B, and 202C (in decrypted form) within acorresponding portion of data repository 134, e.g., within RFP queue135. Executed decomposition engine 146 may also perform operations thataccess mapping data store 138 (e.g., as maintained within datarepository 134), and obtain one or more elements of field mapping data138A that characterize a structure, composition, or format of one ormore data fields of RFP message 202A, 202B, and 202C. For example, andas described herein, each of RFP message 202A, 202B, and 202C mayinclude message fields consistent with the ISO 20022 standard forelectronic data exchange between financial institutions, and each of themessage fields may be populated with data structured and formatted inaccordance with the ISO 20022 standard.

By way of example, user 101 may initiate the $50.00 purchase of theshirt from the first retailer (e.g., “Sam's Haberdashery”) at 10:30 a.m.on May 30, 2022. In some instances, RFP message 202A may be associatedwith a request, from the first retailer on May 30, 2022, for thereal-time payment of $50.00 from user 101 for the purchased shirt, andreferring to FIG. 2B, RFP message 202A may include message field 228populated with a formatted payment date of May 30, 2022 (e.g.,“2022-05-30”), and message fields 230 populated with respective ones ofa formatted payment amount (e.g., “50.00”) and a formatted paymentcurrency (e.g., “USD”).

RFP message 202A may also maintain, within corresponding ones of messagefields 232, a formatted name of user 101 (e.g., “John Q. Stone”) andselected portions of a formatted postal address of user 101 (e.g., “2220Eye Street NW, Washington, D.C., 20037, US”), and may maintain, withinmessage field 234, a formatted identifier of a financial servicesaccount selected by user 101 to fund the $50.00 payment to user 101(e.g., an account number “XXXX-1234-5678-9012” of a deposit accountmaintained by user 101 and the financial institution). Further, asillustrated in FIG. 2B, message fields 236 of RFP message 202A may bepopulated, respectively, with a formatted name of the first retailer(e.g., “Sam's Haberdashery”) and selected portions of a formatted postaladdress of the local home-improvement store (e.g., “3262 M St N.W.,Washington, D.C., 20007”). RFP message 202A may also maintain, withinmessage field 238, a formatted identifier of a financial servicesaccount held by the local home-improvement store and capable ofreceiving proceeds from the requested $50.00 payment (e.g., an accountnumber “XXXX-9012-3456-7890” of a business checking account held by thefirst retailer, etc.).

In some instances, RFP message 202A may also include one or more messagefields that specify structured, or unstructured, remittance informationassociated with the requested, $50.00 real-time payment for the shirtpurchased by user 101 from Sam's Haberdashery on May 30, 2022, such as,but not limited to, a link to invoice data in PDF or HTML format thatidentifies the actual postal address of Sam's Haberdashery, and thatincludes contextual information identifying and characterizing thepurchased shirt (e.g., a unique product identifier of the purchasedshirt, such as a stock keeping unit (SKU), a universal product code(UPC), a product name, etc.) or the $50.00 purchase (e.g., a subtotal ofthe transaction, any applied sales taxes, or any delivery fees, etc.).As described herein, the link may include a long-form or shortened URLassociated with formatted invoice data that that points to a storagelocation of formatted invoice data within a data repository maintainedby one or more computing systems with the first retailer, e.g., merchantsystem 110. For example, message field 244 of RFP message 202A may bepopulated with a long-form URL (e.g.,www.example.com/receipt?custid=′1234′?zip=20007) that points to theformatted invoice data maintained within the data repository of merchantsystem 110, and that includes the actual postal code of the firstretailer (e.g., “20007”) and a customer identifier of user 101 assignedby the first retailer (e.g., “1234”).

The disclosed embodiments are, however, not limited to RFP messagespopulated with these exemplary elements of customer, merchant, payment,transaction, and additional remittance information, and in otherexamples, RFP message 202A may include any additional, or alternate,message fields specified within field mapping data 138A and consistentwith the ISO 20022 standard for electronic data exchange. Further,although not illustrated in FIG. 2B, RFP messages 202B and 202C may alsoinclude elements of customer, counterparty, payment, transaction, andadditional remittance data, and other elements of data, specified withinfield mapping data 138A and consistent with the ISO 20022 standard forelectronic data exchange. By way of example, the message fields of RFPmessage 202B may include elements of customer, merchant, payment,transaction, and additional remittance data that characterize a request,from the second retailer (e.g., “Woody's Suits”) on May 30, 2022, forthe real-time payment of $75.00 from user 101 for the purchase of thepants initiated at 11:15 a.m. on May 30, 2022, and the message fields ofRFP message 202C may include elements of customer, counterparty,payment, transaction, and additional remittance data that characterize arequest, from the third retailer (e.g., “Claude's Clothing”) on May 30,2022, for the real-time payment of $175.00 from user 101 for thepurchase of the jacket initiated at 12:30 p.m. on May 30, 2022.

Referring back to FIG. 2A, and based on the obtained elements of fieldmapping data 138A, executed decomposition engine 146 may performoperations that parse RFP message 202A and obtain elements of decomposedfield data 204 that identify and characterize the customer (e.g., user101 of client device 102), the counterparty (e.g., the first retailer,“Sam's Haberdashery”), the requested, real-time payment, and theinitiated purchase transaction. In some instances, and through theperformance of these exemplary operations, executed decomposition engine146 may “decompose” the structured or unstructured data populating themessage fields of RFP message 202A in accordance with field mapping data138A, and generate the elements of decomposed field data 204 thatinclude, but are not limited to, one or more elements of customer data206, payment data 208, transaction data 210, and counterparty data 212.

By way of example, and based on the elements of field mapping data 138A,executed decomposition engine 146 may determine that message fields 232of RFP message 202A include data that identifies and characterizes user101, and may perform operations that obtain the customer name of user101 (e.g., “John Q. Stone”) and the postal address associated with user101 (e.g., “2220 Eye Street NW, Washington, D.C., 20037, US”) frommessage fields 232 of RFP message 202A, and that package the obtainedcustomer name and postal address into corresponding portions of customerdata 206 of decomposed field data 204. Additionally, and based on theelements of field mapping data 138A, executed decomposition engine 146may determine that message fields 236 of RFP message 202A includes dataidentifying and characterizing the first retailer, and may performoperations that obtain the name of the first retailer (e.g., “Sam'sHaberdashery”) and the postal address associated with the first retailer(e.g., “3262 M St N.W., Washington, D.C., 20007”) from message fields236, and that package the obtained name and postal address of the firstretailer into corresponding portions of counterparty data 212 withindecomposed field data 204.

In some instances, and based on the elements of field mapping data 138A,executed decomposition engine 146 may determine that one or moreadditional message fields of RFP message 202A include elements of dataidentifying and characterizing the requested payment, such as, but notlimited to: message field 228, which includes the requested payment date(e.g., “May 30, 2022”); message fields 230, which includes dataidentifying the payment amount of the requested payment (e.g., $50.00)and the requested payment currency (e.g., “USD”); message field 234,which includes an identifier of the payment instrument selected by user101 to fund the initiated purchase transaction (e.g., the account number“XXXX-1234-5678-9012”); and message field 238, which includes anidentifier of an account associated with the first retailer andavailable of receiving the requested payment (e.g., the tokenizedaccount number “XXXX-9012-3456-7890,” etc.). Executed decompositionengine 146 may perform operations that extract the payment amount, theaccount identifiers, and the requested payment date from correspondingones of message fields 228, 230, and 238, and that package the extractedpayment amount, the identifiers of the selected payment instrument andthe account associated with the first retailer, and the requestedpayment date into corresponding portions of payment data 208.

Further, and based on the elements of field mapping data 138A, executeddecomposition engine 146 may also determine that one or more additional,or alternate, message fields of RFP message 202A (not illustrated inFIG. 2B) include elements of transaction data that characterize theinitiates purchase transaction, e.g., the $50.00 purchase of the shirtat 10:30 a.m. on May 30, 2022, from Sam's Haberdashery in Washington,D.C. By way of example, the transaction data maintained within theadditional, or alternate, message fields of RFP message 202A may includeone or more identifiers of the purchased shirt (e.g., the product name,the corresponding UPC or SKU, etc.), the 10:30 a.m. transaction time,the May 30, 2022, transaction date, the total transaction amount of$50.00, a subtotal of $45.00 for the shirt, and $5.00 in imposed localsales tax. In some instances, executed decomposition engine 146 mayperform operations that extract the elements of transaction data, suchas, but not limited to, the exemplary elements described herein, fromcorresponding ones of the additional, or alternate, message fields, andpackage the extracted elements of transaction data into correspondingportions of transaction data 210 of decomposed field data 204.

Executed decomposition engine 146 may also determine, based on theelements of field mapping data 138A, that message field 244 of RFPmessage 202A includes structured or unstructured elements of remittancedata that characterizes further the requested payment, the initiatedtransaction, user 101, or the first retailer, and executed decompositionengine 146 may obtain the structured or unstructured elements ofremittance data from message field 244 of RFP message 202A and packagethe elements of remittance data into corresponding portions ofremittance information 214. In some instances, the elements ofstructured or unstructured remittance data may include a link (e.g., ashortened or tiny URL, a long-form URL, etc.) to formatted invoice dataassociated with the requested payment and maintained by a correspondingcomputing system associated with the first retailer, such as merchantsystem 110. For example, the remittance data may include a long-form URL216 (e.g., www.example.com/receipt?custid=′1234′?zip=20007), that pointsto formatted invoice data 218 within the one or more tangible,non-transitory memories of merchant system 110, and that includes, amongother things, the actual postal code of the first retailer (e.g.,“20007”) and a customer identifier of user 101 assigned by the firstretailer (e.g., “1234”).

In some instances, the one or more processors of FI computing system 130may execute a remittance analysis engine 240, which may performoperations that, based on URL 216 maintained within remittanceinformation 214 of decomposed field data 204, programmatically accesselements of formatted invoice data 218 maintained within a datarepository 220 at merchant system 110, and that process the accessedelements of formatted invoice data 218 to obtain additional, oralternate, elements of customer data 206, payment data 208, transactiondata 210, counterparty data 212 that further characterize the initiatedpurchase transaction or the purchased products or services associatedwith the requested, real-time payment, e.g., the initiated $50.00purchase of the shirt from the first retailer (e.g., “Sam'sHaberdashery”) on May 30, 2022, at 10:30 a.m. Examples of the additionalelements of transaction data 210 may include, but are not limited to,one or more identifiers of the purchased shirt (e.g., the product name,the corresponding UPC or SKU, etc.), the 10:30 a.m. transaction time,the May 30, 2022, transaction date, the total transaction amount of$50.00, a subtotal of $45.00 for the hardwood flooring, or the $5.00 inimposed local sales tax.

For example, remittance analysis engine 240 may access URL 216maintained within remittance information 214, and may process URL 216and generate a corresponding HTTP request 248 for the elements offormatted invoice data 218 maintained at merchant system 110. Executedremittance analysis engine 240 may also perform operations that cause FIcomputing system 130 to transmit HTTP request 248 across network 120 tomerchant system 110. Merchant system 110 may, for example, receive HTTPrequest 248, and based on portions of HTTP request 248 and linking data250 maintained within data repository 220 (e.g., based on a determinedmatch or correspondence between the portions of HTTP request 248 andlinking data 250), merchant system 110 may perform operations thatobtain the elements of formatted invoice data 218 from data repository220, and that transmit the elements of formatted invoice data 218 acrossnetwork 120 to FI computing system 130, e.g., as a response to HTTPrequest 248.

Executed remittance analysis engine 240 may receive the elements offormatted invoice data 218 from merchant system 110, and may perform anyof the exemplary processes described herein to parse the elements offormatted invoice data 218 (e.g., in a received format, such as a PDF orHTML form, or in a transformed or enhanced format, etc.) and obtain,from the parsed elements of formatted invoice data 218, one or more ofthe additional, or alternate, elements of customer data 206, paymentdata 208, transaction data 210, or counterparty data 212. By way ofexample, executed remittance analysis engine 240 may apply one or moreoptical character recognition (OCR) processes or optical wordrecognition (OWR) processes to the elements of formatted invoice data218 in PDF form to generate, or obtain, elements of textual contentrepresentative of the data that characterize user 101, the firstretailer, the requested payment, or the initiated purchase of the shirt.

By way of example, executed remittance analysis engine 240 may performoperations that detect a presence one or more keywords within thegenerated elements of textual content (e.g., “time,” “shirt,” “date,”“UPC,” etc.), and may extract elements of the textual content associatedwith these keywords as corresponding ones of the additional, oralternate, elements of customer data 206, payment data 208, transactiondata 210, or counterparty data 212. In other examples, executedremittance analysis engine 240 may detect a presence of the additional,or alternate, elements of customer data 206, payment data 208,transaction data 210, or counterparty data 212 within the generatedtextual content based on an application of one or more adaptivelytrained machine learning or artificial intelligence models to portionsof the textual content, and examples of these adaptively trained machinelearning or artificial intelligence models includes a trained neuralnetwork process (e.g., a convolutional neural network, etc.) or adecision-tree process that ingests input datasets composed of all, orselected portions, of the textual content. The disclosed embodimentsare, however, not limited to exemplary processes for detecting andextracting one or more of the additional, or alternate, elements ofcustomer data 206, payment data 208, transaction data 210, orcounterparty data 212 from the generated textual content, and in otherinstances, executed remittance analysis engine 240 may perform anyadditional, or alternate, process for identifying one or more of theadditional, or alternate, elements of customer data 206, payment data208, transaction data 210, or counterparty data 212 within the textualcontent derived from the processing of the elements of formatted invoicedata 218 in PDF format.

Further, and as described herein, the elements of formatted invoice data218 may be structured in HTML form, and may include metadata thatidentify and characterize user 101 (e.g., the customer name, etc.), thefirst retailer (e.g., the name or other identifier, etc.), the requestedpayment (e.g., a payment amount, etc.), or the initiated purchase of theshirt. Executed remittance analysis engine 240 may perform operationsthat detect one or more of the elements of metadata within the elementsof formatted invoice data 218, and that obtain, from the elements ofmetadata, additional, or alternate, elements of customer data 206,payment data 208, transaction data 210, or counterparty data 212, asdescribed herein. The disclosed embodiments are, however, not limited tothese exemplary processes for detecting and extracting the additional,or alternate, elements of customer data 206, payment data 208,transaction data 210, or counterparty data 212 from HTML-formattedinvoice data 218, and in other instances, executed remittance analysisengine 240 may perform any additional, or alternate, process detectingand obtaining data from the elements of formatted invoice data 218structured in HTML form, including, but not limited to, an applicationof one or more screen-scraping processes to elements of formattedinvoice data 218 structured in HTML form.

In some instances, executed remittance analysis engine 240 may alsoperform any of the exemplary operations described herein to process theelements of formatted invoice data 218, structured in PDF or HTML form,and extract one or more of the additional elements of transaction datathat further characterize the $50.00 purchase of the shirt from Sam'sHaberdashery on May 30, 2022, at 10:30 a.m. By way of example, theadditional elements of transaction data may include, but are not limitedto, one or more identifiers of the purchased shirt (e.g., the productname, the corresponding UPC or SKU, etc.), the 10:30 a.m. transactiontime, the May 30, 2022, transaction date, the total transaction amountof $50.00, a subtotal of $45.00 for the shirt, and the $5.00 in imposedlocal sales tax. Executed remittance analysis engine 240 may performoperations that store the additional elements of transaction data withinportions of transaction data 210 of decomposed field data 204.

In some instances, executed decomposition engine 146 may performoperations that store decomposed field data 204, which includes theelement of customer data 206, payment data 208, transaction data 210, orcounterparty data 212, and remittance information 214 (including URL216), within a corresponding portion of data repository 134, forexample, in conjunction with RFP message 202A within a portion of RTPdata store 144. Further, and based on the elements of field mapping data138A, executed decomposition engine 146 may perform also perform any ofthe exemplary processes described herein to operations that parse eachof RFP messages 202B and 202C, and obtain, respectively, elements ofdecomposed field data 252 and decomposed field data 266 that identifyand characterize the customer (e.g., user 101 of client device 102), acorresponding counterparty (e.g., the second retailer, “Woody's Suits,”and the third retailer, “Claude's Clothes,” respectively), correspondingones of the requested, real-time payments, and corresponding ones of theinitiated purchase transactions.

By way of example, and based on the elements of field mapping data 138A,executed decomposition engine 146 may perform any of the exemplaryprocesses described herein to parse the elements of RFP message 202B,and obtain the customer name, the postal address of user 101, and a nameof the second retailer (“Woody's Suits”) and a postal address associatedwith the second retailer (e.g., “3320 Cady's Alley N.W., Washington,D.C., 20007”), and to package the name and postal address of user 101,and the name and postal address of the second retailer, withinrespective portions of customer data 254 and counterparty data 260 ofdecomposed field data 25. Further, and based on the elements of fieldmapping data 138A, executed decomposition engine 146 may perform any ofthe exemplary processes described herein to obtain, from one or moreadditional message fields of RFP message 202B, the requested paymentdate (e.g., “May 30, 2022”), the requested payment amount (e.g., $75.00)and the requested payment currency (e.g., “USD”), an identifier of thepayment instrument selected by user 101 to fund the initiated purchasetransaction (e.g., the account number “XXXX-1234-5678-9012” of thedeposit account), and an identifier of an account associated with thesecond retailer and available to receive the requested $75.00 payment.As described herein, executed decomposition engine 146 may performoperations that package the requested payment date, the requestedpayment amount (and the requested payment currency), and the identifiersof the payment instrument of user 101 and the account of the secondretailer within corresponding portions of payment data 256 of decomposedfield data 252.

Executed decomposition engine 146 may also perform any of the exemplaryprocesses described herein that, based on the elements of field mappingdata 138A, obtain elements of transaction data that characterize thesecond purchase transaction, e.g., the $75.00 purchase of the pants at11:15 a.m. on May 30, 2022, from Woody's Suits in Washington, D.C.(e.g., one or more of the exemplary elements of transaction datadescribed herein), and that package the obtained elements of transactiondata within corresponding portions of transaction data 258 of decomposedfield data 252. Further, executed decomposition engine 146 may alsodetermine, based on the elements of field mapping data 138A, that one ormore message fields of RFP message 202B includes structured orunstructured elements of remittance data that characterizes further therequested payment, the second purchase transaction, user 101, or thesecond retailer, and executed decomposition engine 146 may obtain thestructured or unstructured elements of remittance data from the one ormore message fields of RFP message 202B and package the obtainedelements of remittance data into corresponding portions of remittanceinformation 262 of decomposed field data 252.

By way of example, the structured or unstructured elements of remittancedata maintained within the message fields of RFP message 202B mayinclude a link (e.g., a shortened or tiny URL, a long-form URL, etc.) toformatted invoice data associated with the requested $75.00 payment andmaintained by a corresponding computing system associated with thesecond retailer, such as merchant system 114. Although not illustratedin FIG. 2A, executed remittance analysis engine 240 may perform any ofthe exemplary processes described herein to programmatically access theelements of formatted invoice data associated with the requested $75.00payment that are maintained at merchant system 114, to process theformatted invoice data (e.g., in PDF or HTML form), and to obtain one ormore additional elements of customer data 254, payment data 256,transaction data 258, or counterparty data 260. As described herein, theadditional elements of transaction data 258 may include, but are notlimited to, one or more identifiers of the purchased pants (e.g., aproduct name, a corresponding UPC or SKU, etc.), the 11:15 a.m.transaction time, the May 30, 2022, transaction date, the totaltransaction amount of $75.00, a subtotal of $67.50, and $7.50 in imposedlocal sales tax.

Further, and based on the elements of field mapping data 138A, executeddecomposition engine 146 may also perform any of the exemplary processesdescribed herein to parse the elements of RFP message 202C, and obtainthe customer name, the postal address of user 101, and a name of thethird retailer (“Claude's Clothing”) and a postal address associatedwith the third retailer (e.g., “3077 M St N.W., Washington, D.C.,20007”), and to package the name and postal address of user 101, and thename and postal address of the third retailer, within respectiveportions of customer data 268 and counterparty data 274 of decomposedfield data 266. Further, and based on the elements of field mapping data138A, executed decomposition engine 146 may perform any of the exemplaryprocesses described herein to obtain, from one or more additionalmessage fields of RFP message 202C, the requested payment date (e.g.,“May 30, 2022”), the requested payment amount (e.g., $175.00) and therequested payment currency (e.g., “USD”), an identifier of the paymentinstrument selected by user 101 to fund the third purchase transaction(e.g., the account number “XXXX-1234-5678-9012” of the deposit account),and an identifier of an account associated with the third retaileravailable to receive the requested $175.00 payment. As described herein,executed decomposition engine 146 may perform operations that packagethe requested payment date, the requested payment amount (and therequested payment currency), and the identifiers of the paymentinstrument of user 101 and the account of the third retailer withincorresponding portions of payment data 270 of decomposed field data 266.

Executed decomposition engine 146 may also perform any of the exemplaryprocesses described herein that, based on the elements of field mappingdata 138A, obtain, from the message field of RFP message 202C, elementsof transaction data that characterize the third purchased transaction,e.g., the $175.00 purchase of the jacket at 12:30 p.m. on May 30, 2022,from Claude's Clothing in Washington, D.C., and that package theobtained elements of transaction data within corresponding portions oftransaction data 272 of decomposed field data 266. Further, executeddecomposition engine 146 may also determine, based on the elements offield mapping data 138A, that one or more message fields of RFP message202C include structured or unstructured elements of remittance data thatcharacterizes further the requested payment, the third purchasetransaction, user 101, or the third retailer, and executed decompositionengine 146 may obtain the structured or unstructured elements ofremittance data from the one or more message fields of RFP message 202Cand package the obtained elements of remittance data into correspondingportions of remittance information 276 of decomposed field data 266.

By way of example, the structured or unstructured elements of remittancedata maintained within the message fields of RFP message 202B mayinclude a link (e.g., a shortened or tiny URL, a long-form URL, etc.) toformatted invoice data associated with the requested $175.00 payment andmaintained by a corresponding computing system associated with the thirdretailer, such as merchant system 118. In some instances, executedremittance analysis engine 240 may perform any of the exemplaryprocesses described herein to programmatically access the elements offormatted invoice data associated with the requested $175.00 paymentthat are maintained at merchant system 118, to process the formattedinvoice data (e.g., in PDF or HTML form), and to obtain one or moreadditional elements of customer data 268, payment data 270, transactiondata 272, or counterparty data 274 of decomposed field data 266. Asdescribed herein, the additional elements of transaction data 272 mayinclude, but are not limited to, one or more identifiers of thepurchased jacket (e.g., a product name, a corresponding UPC or SKU,etc.), the 12:30 p.m. transaction time, the May 30, 2022, transactiondate, the total transaction amount of $175.00, a subtotal of $157.50,and $17.50 in imposed local sales tax.

In some instances, executed decomposition engine 146 may performoperations that store decomposed field data 252, which includes theelements of customer data 254, payment data 256, transaction data 258,counterparty data 260, and remittance information 262, within acorresponding portion of data repository 134, for example, inconjunction with RFP message 202B within a portion of RTP data store144. Further, executed decomposition engine 146 may also performoperations that store decomposed field data 266, which includes theelement of customer data 268, payment data 270, transaction data 272,counterparty data 274, and remittance information 276, within acorresponding portion of data repository 134, for example, inconjunction with RFP message 202C within a portion of RFP data store144. Further, executed decomposition engine 146 may also perform any ofthe exemplary processes described herein, either alone or in conjunctionwith executed remittance analysis engine 240, to “decompose” one or moreadditional, or alternate, RFP messages maintained within RFP queue 135into elements of decomposed field data that include correspondingelements of customer data, counterparty data, payment data, transactiondata, and remittance information, and to store the correspondingelements of decomposed field data within a corresponding portion of datarepository 134, such as within RTP data store 144.

Executed decomposition engine 146 may also perform operations thatprovide the elements of decomposed field data associated with one ormore of the RFP messages maintained within RFP queue 135, includingelements of decomposed field data 204, 252, and 266 associated withcorresponding ones of RFP messages 202A, 202B, and 202C, as inputs toanalytical engine 148 executed by the one or more processors of FIcomputing system 130. Upon execution by the one or more processors of FIcomputing system 130, executed analytical engine 148 may perform any ofthe exemplary processes described herein that, based on an analysis ofthe elements of decomposed field data 204, 252, and 258 characterizingrespective ones of the first, second, and third purchase transactionsinitiated by user 101 during a prior temporal interval (e.g., a two-hourinterval between 10:30 a.m. and 12:30 p.m. on May 30, 2022), predict anoccurrence of one or more additional purchases transactions during afuture temporal interval that would be consistent with a transactionalbehavioral of user 101 during the prior temporal interval. Executedanalytical engine 148 may also perform any of the exemplary processesdescribed processes described herein to “pre-approve” user 101 for anavailable credit or loan product, such as an unsecured installment loanor an unsecured personal loan, in an amount sufficient to fund both thepurchase transactions initiated by user 101 during the prior temporalinterval and the predicted occurrences of the additional purchasetransactions during the future temporal interval based on, among otherthings, an application of one or more internal qualification orunderwriting criteria to data characterizing user 101, and interactionsbetween user 101 and the financial institution or one or more unrelatedfinancial institution, and a use, or misuse, of financial productprovisioned by the financial institution of the unrelated financialinstitutions.

Referring to FIG. 3A, executed analytical engine 148 may receive theelements of decomposed field data 204, 252, and 266 (e.g., associatedwith corresponding ones of RFP messages 202A, 202B, and 202C) fromexecuted decomposition engine 146. In some instances, a predictivemodule 302 of executed analytical engine 148 may perform operationsthat, among other things, access the elements of transaction andcounterparty data maintained within each of decomposed field data 204,252, and 266, and obtain information that characterizes the each of thecounterparties involved the purchase transactions initiated by user 101during the prior temporal interval, and values of parameters thatcharacterize each of the purchase transactions initiated by user 101during the prior temporal interval. The obtained counterpartyinformation, for example, include a counterparty identifier of each ofthe counterparties (e.g., a counterparty name, a standard industrialclassification (SIC) code, etc.) and geographic data characterizing eachof the counterparties (e.g., a portion of a corresponding postaladdress, etc.), and parameter values may include, but are not limited, atransaction date or time, a transaction value, and identifiers of one ormore products or services involved in corresponding ones of the first,second, and third purchase transactions.

By way of example, executed predictive module 302 may obtain, from theelements of counterparty data 212 maintained within decomposed fielddata 204, a counterparty name of the first retailer (e.g., “Sam'sHaberdashery”) and the postal address of the first retailer (e.g., “3262M St N.W., Washington, D.C., 20007”), and may obtain, from the elementsof transaction data 210 maintained within decomposed field data 204,transaction parameter values for the first purchase transaction thatinclude the 10:30 a.m. transaction time, the May 30, 2022, transactiondate, the $50.00 transaction amount, and the product identifier of thepurchased shirt (e.g., the product name, the UPC or SKU, etc.). Executedpredictive module 302 may also obtain, from the elements of counterpartydata 260 maintained within decomposed field data 252, a counterpartyname of the second retailer (e.g., “Woody's Suits”) and the postaladdress of the first retailer (e.g., “3320 Cady's Alley N.W.,Washington, D.C., 20007”), and may obtain, from the elements oftransaction data 258 maintained within decomposed field data 252,transaction parameter values of the first purchase transaction thatinclude the 11:15 a.m. transaction time, the May 30, 2022, transactiondate, the $75.00 transaction amount, and the product identifier of thepurchased pants (e.g., the product name, the UPC or SKU, etc.).

Further, executed predictive module 302 may obtain, from the elements ofcounterparty data 274 maintained within decomposed field data 266, acounterparty name of the third retailer (e.g., “Claude's Clothing”) andthe postal address of the first retailer (e.g., “3077 M St N.W.Washington, D.C., 20007”), and may obtain, from the elements oftransaction data 258 maintained within decomposed field data 252,transaction parameter values of the third purchase transaction thatinclude the 12:30 p.m. transaction time, the May 30, 2022, transactiondate, the $175.00 transaction amount, and the product identifier of thepurchased jacket (e.g., the product name, the UPC or SKU, etc.). In someinstances, the obtained elements of counterparty data (e.g., the namesof the first, second, and third retailers and the geographic datacharacterizing the first, second, and third retailers) and the obtainedparameter values that characterize the first, second, and third purchasetransactions may characterize a spending or transaction behavior of user01 across the prior temporal intervals.

Further, in some instances, executed predictive module 302 may alsoperform operations that, based on the elements of customer profile data140A maintained within customer data store 140, identify additionalcustomers of the financial institution that are characterized by a valueof one or more demographic parameters that are similar to acorresponding parameter value that characterizes user 101 (e.g., thatform a “customer peer group” of user 101). By way of example, thedemographic parameters may include, among other things, a customer ageor a city of residence of customer, and a value of one of thedemographic parameters that characterizes one of the additionalcustomers may be similar to a corresponding parameter value thatcharacterizes user 101 when the parameter value is equivalent to thecorresponding parameter value, when parameter value falls within apredetermined, threshold amount of the corresponding parameter value, orwhen the parameter value is disposed within a specified value range thatincludes the corresponding parameter value.

By way of example, user 101 may represent a forty-four-year-old residentof Washington, D.C., and the additional customers within the customerpeer group of user 101 may reside in Washington, D.C., and may becharacterized by an age ranging from thirty-five and forty-four. In someinstances, executed predictive module 302 may perform operations thatobtain, from the elements of customer profile data 140A, a uniquecustomer identifier of each of the additional customers within thecustomer peer group (e.g., a customer-specific, alphanumeric identifierassigned by the financial institution), and based on the unique customeridentifier associated with each of the customers, obtain one or moreelements of account data 140B, transaction data 140C, and third-partydata 140D that characterize each of the additional customers during acurrent temporal interval and/or across one or more temporal intervals.The obtained elements of account data 140B, transaction data 140C, andthird-party data 140D obtained for each of the additional customers(e.g., customer-specific elements of peer interaction data 304 of FIG.3A) may characterize the interactions of each of the additional customerwithin the financial institution, with one or more unrelated financialinstitutions, or with financial products provisioned by the financialinstitution or with the unrelated financial institution during thecurrent or one or more prior temporal intervals, and in some instances,may characterize a spending or transactional behavior of theseadditional customers during one or more temporal intervals.

Referring back to FIG. 3A, executed predictive module 302 may performoperations that analyze the elements of counterparty and the transactionparameter values that characterize the first, second, and third purchasetransactions initiated by user 101 during the prior temporal interval,and in some instances, the customer-specific elements of peerinteraction data 304 associated with one or more of the additionalcustomers that establish the customer peer group of user 101. Based onthe analysis, executed predictive module 302 may perform operations thatpredict an occurrence of one or more additional purchases transactionsduring a future temporal interval that would be consistent with thespending or transactional behavioral of user 101 during the priortemporal interval, and that generate elements of predicted output data306 that include a value of one or more parameters that characterizeeach of the predicted occurrence of the additional purchasestransactions during the future temporal interval.

In some instances, executed predictive module 302 may perform operationsthat apply trained machine learning or artificial intelligence processto an input dataset obtained, or extracted from, the elements ofcounterparty and the transaction parameter values that characterize thefirst, second, and third purchase transactions initiated by user 101during the prior temporal interval and in some instances, one or more ofthe customer-specific elements of peer interaction data 304. Based onthe application of the trained machine learning or artificialintelligence process to the input dataset, executed predictive module302 may generate elements of predicted output data 306 that include, foreach of the predicted occurrences of the additional purchasetransactions within the future temporal interval, transaction parametervalues that include, but are not limited to, a transaction amount, anidentifier of a corresponding product or service, a transactionlocation, and/or an identifier of a corresponding counterparty, such asan additional retailer.

As described herein, examples of the trained machine-learning andartificial-intelligence processes may include, but are not limited to, aclustering process, an unsupervised learning process (e.g., a k-meansalgorithm, a mixture model, a hierarchical clustering algorithm, etc.),a semi-supervised learning process, a supervised learning process, or astatistical process (e.g., a multinomial logistic regression model,etc.). The trained machine-learning and artificial-intelligenceprocesses may also include, among other things, a decision tree process(e.g., a boosted decision tree algorithm, etc.), a random decisionforest, an artificial neural network, a deep neural network, or anassociation-rule process (e.g., an Apriori algorithm, an Eclatalgorithm, or an FP-growth algorithm). Further, and as described herein,each of these exemplary machine-learning and artificial-intelligenceprocesses may be trained against, and adaptively improved using,elements of training and validation data, and may be deemed successfullytrained and ready for deployment when a value of one or more performanceor accuracy metrics are consistent with one or more threshold trainingor validation criteria.

For instance, the additional trained machine learning or artificialintelligence process may include a trained decision-tree process, andexecuted predictive module 302 may obtain, from one or more tangible,non-transitory memories, elements of process input data and processparameter data associated with the trained decision-tree process, suchas those described herein (not illustrated in FIG. 3A). In someexamples, not illustrated in FIG. 3A, executed predictive module 302 mayperform operations that generate one or more discrete elements (e.g.,“feature values”) of the input dataset in accordance with the elementsof process input data and based on the elements of counterparty and thetransaction parameter values that characterize the first, second, andthird purchase transactions initiated by user 101 during the priortemporal interval and/or one or more of the customer-specific elementsof peer interaction data 304. Based on portions of the process parameterdata, executed predictive module 302 may perform operations thatestablish a plurality of nodes and a plurality of decision trees for thetrained decision-tree process, each of which receive, as inputs (e.g.,“ingest”), corresponding elements of the input dataset. Upon ingestionof the input dataset by the established nodes and decision trees of thetrained decision-tree process, executed predictive module 302 mayperform operations that apply the additional, trained, decision-treeprocess to the input dataset, and that generate the elements ofpredicted output data 306 that include the predicted transactionparameter values characterizing each of the predicted occurrences of theadditional purchase transactions within the future temporal interval,and in some instances, a total transaction amount characterizing thosepurchase transactions initiated during the prior temporal interval andthe predicted occurrences of the additional purchase transactions duringthe future temporal interval.

By way of example, user 101 initiated the first purchase transaction forthe $50.00 shirt from the first retailer (e.g., “Sam's Haberdashery”) at10:30 a.m. on May 30, 2022, the second purchase transaction for the$75.00 pants from the second retailer (e.g., “Woody's Suits”) at 11:15a.m. on May 30, 2022, and the third purchase transaction for the $175.00jacket from the third retailer (“Claude's Clothes”) at 12:30 p.m. on May30, 2022. In some instances, based on the elements of counterparty andthe transaction parameter values that characterize the first, second,and third purchase transactions initiated by user 101 during the prior,two-hour temporal interval and in some instances, one or more of thecustomer-specific elements of peer interaction data 304, executedprediction module 302 may perform any of the exemplary processesdescribed herein to establish that user 101 intended to purchase apparelfrom retailers disposed within the Georgetown neighborhood ofWashington, D.C. (e.g., postal code 20007), and further, to predict anoccurrence of at least one additional purchase transaction for shoes inan amount of $75.00 from a fourth retailer in the Georgetownneighborhood of Washington, D.C., during a future temporal interval,e.g., a temporal interval extending until 5:00 p.m. on May 30, 2022.

In some instances, the predicted occurrence of the at least oneadditional purchase transaction (e.g., the purchase of the shoes in theamount of $75.00) may be consistent with determined intent of user 101(e.g., the purchase of apparel within the Georgetown neighborhood ofWashington, D.C.), and executed predictive module 302 may generateelements of predicted output data 306 that specify values of one or moretransaction parameters that characterize the predicted occurrence of theadditional purchase transaction during the future temporal interval,such as, but not limited to, the $75.00 transaction amount, anidentifier of the product involved in the additional purchasetransaction (e.g., a product name for the shoes), and geographic datacharacterizing the additional purchase transaction (e.g., the postalcode “20007”). Further, the elements of predicted output data 306 mayalso include an aggregate transaction amount of $375.00 attributable tothe first, second, and third purchase transactions initiated by user 101during the prior temporal interval (e.g., an aggregate transactionamount of $300.00) and the predicted occurrence of the additionalpurchase transaction during the future temporal interval (e.g., $75.00).

As illustrated in FIG. 3A, executed predictive module 302 may providethe elements of predicted output data 306 as an input to a pre-approvalmodule 308 of executed analytical engine 148. Upon execution by the oneor more processors of FI computing system 130, executed pre-approvalmodule 308 may access candidate financial product data store 136 (e.g.,as maintained within data repository 134 of FIG. 1 ), and obtaininformation identifying a candidate credit or loan products that isavailable for provisioning to user 101 and, if provisioned, would beappropriate to fund not only the $300 transaction amount associated withthe first, second, and third purchase transactions initiated during theprior, two-hour temporal interval, but also the predicted occurrence ofthe additional, $75.00 purchase transaction during the future temporalinterval. Examples of these candidate credit or loan products include,but are not limited to, an unsecured personal loan or installment loanhaving a fixed or variable interest rate, a line-of-credit securedagainst other customer asserts, or other secured or unsecured creditproducts, and the information obtained from candidate financial productdata store 136 may, for the candidate credit or loan product, includeone or more internal qualification or underwriting criteria thatestablish an availability of the candidate credit or loan product touser 101 and enable the financial institution to establish customer- orproduct-specific terms and conditions.

By way of example, executed pre-approval module 308 may accessthird-party data 140D (e.g., as maintained within customer data store140), and obtain one or more elements of credit-bureau data 310associated with user 101, such as, but not limited to, a credit score ora number of credit inquiries during a current or prior temporalinterval. Executed pre-approval module 308 may also access account data140B and transaction data 140C, and may obtain information thatcharacterize interactions between user 101 and the financialinstitution, and with financial products provisioned by the financialinstitution, across a current and one or more prior temporal intervals(e.g., that characterize a spending or transactional behavior duringthese temporal intervals).

The information obtained from account data 140B may, in some instances,include elements of interaction data 312 that characterize interactionsbetween user 101 and financial products issued by the financialinstitution across one or more temporal intervals. For example,interaction data 312 may include, among other things, outstandingbalances associated with one or more of the financial products (e.g.,outstanding balances associated with one or more credit-card accounts orunsecured personal loans, etc.), a current status of one or more of thefinancial products (e.g., no overdue balances, delinquent, default,etc.), and cashflow metrics associated with one or more of the financialproducts (e.g., a net cashflow though a deposit account, etc.). Further,in some instances, executed pre-approval module 308 may also obtainelements of comparative data 314 that identify, characterize, andcompare seasonal or year-over-year variations in a spending andtransactional habit of user 101. By way of example, comparative data 314may include the aggregate transaction amount of $300.00 due to thefirst, second, and third purchase transactions initiated by user 101 onMay 30, 2022, and aggregate transactions amounts associated withpurchase transactions initiated by user 101 on May 30, 2021, and May 30,2022, and may enable executed pre-approval module 308 to determinewhether the aggregate transaction amount of $300.00 is consistent with,or an outlier from, a seasonal transactional behavior of user 101, e.g.,on May 30, 2022.

Referring back to FIG. 3A, executed pre-approval module 308 may obtain,from candidate financial product data store 136, a product identifier316 of a particular one of the credit or loan products, such as anunsecured installment loan characterized by a fixed interest rate, andone or more internal qualification or underwriting criteria 318 thatestablish an availability of the unsecured installment loan to user 101and enable the financial institution to establish customer- orproduct-specific terms and conditions for the unsecured installmentloan. In some instances, and based on an application of one or moreinternal qualification or underwriting criteria 318 to the elements ofpredicted output data 306 (e.g., including the $75.00 transaction amountassociated with the predicted occurrence of the additional purchasetransaction during the future temporal interval and the $375.00aggregate transaction amount across the prior and future temporalintervals), the elements of credit-bureau data 310, and/or on theinformation characterizing the interactions between user 101 and thefinancial institution and/or with the financial products provisioned bythe financial institution (including the elements of interaction data312 and comparative data 314), executed pre-approval module 308 mayestablish that one of the unsecured, fixed-rate installment loan isavailable for provisioning to user 101 and available to fund the $375.00aggregate transaction amount, and may determine one or morecustomer-specific terms and conditions of the available, unsecured,fixed-rate installment loan.

By way of example, and in accordance with the one or more internalqualification or underwriting criteria 318, executed pre-approval module308 may perform operations that pre-approve the provisioning of theunsecured, fixed-rate installment loan in amount of $375.00 (e.g., theaggregate transaction amount across the prior and future temporalintervals) and based on determined terms and conditions that include,but are not limited to, a fixed interest rate of 6.0%, a five-monthterm, and five, equal monthly payments of $76.00 (including $75.00 inprincipal and $1.00 in interest). In some instances, executedpre-approval module 308 may package information characterizing thedetermined terms and conditions of the pre-approved, unsecured,fixed-rate installment loan, including the $375.00 loan amount, thefixed, 6.0% interest rate, the five-month term, and the $76.00 monthlypayments, into corresponding elements of pre-approval data 320. Further,as illustrated in FIG. 3A, executed pre-approval module 308 may alsopackage product identifier 316 of the unsecured, fixed-rate installmentloan (e.g., product name, etc.), and the elements of pre-approval data320 into corresponding portions of pre-approved product data 322, whichexecuted pre-approval module 308 may provide as an input to notificationengine 150.

Upon execution by the one or more processors of FI computing system 130,executed notification engine 150 may perform any of the exemplaryprocesses described herein to generate elements of notification datathat identify, and characterize, the available, unsecured installmentloan, the loan amount of $375.00, and the pre-approved terms andconditions (e.g., the fixed, 6.0% interest rate, the five-month term,and the $76.00 monthly payment), and to transmit the elements ofnotification data across network 120 to client device 102, e.g., forpresentation within a corresponding digital interface. In someinstances, executed notification engine 150 may generate and transmitthe elements of notification data to client device 102, and clientdevice 102 may perform operations that render the elements ofnotification data for presentation within the digital interface, inreal-time and contemporaneously with the receipt of RFP messages 202A,202B, and 202C, and prior to an execution of all, or at least one of,the real-time payments requested by corresponding ones of RFP messages202A, 202B, and 202C, and prior to an initiation of the predicted,additional purchase transaction during the future temporal interval.Certain of these exemplary processes, when implemented collectively byFI computing system 130 and client device 102, may present to user 101 atargeted offer of a directed loan product capable of funding not onlythe one or more purchase transactions initiated during the priortemporal interval, but also one or more predicted occurrences ofpurchase transactions consistent with a determined intent of theinitiated purchase transactions, in real-time during, or prior to, aninitiation of the one or more additional purchase transactions duringthe future temporal interval, e.g., while user 101 shops for shoes inthe Georgetown neighborhood of Washington, D.C., to complete thepreviously purchased shirt, pants, and jacket.

Referring to FIG. 3B, executed notification engine 150 may receivepre-approved product data 322 from executed analytical engine 148, andexecuted notification engine 150 may perform operations that access RTPdata store 144 and obtain elements of decomposed field data 204, 252,and 266 associated with corresponding ones of RFP messages 202A, 202B,and 202C. As described herein, decomposed field data 204 may includeelements of customer data 206, payment data 208, transaction data 210,counterparty data 212, and remittance information 214 that collectivelyidentify and characterize the request, from the first retailer (e.g.,“Sam's Haberdashery”), for the real-time payment of $50.00 from user 101for the purchase of the shirt initiated at 10:30 a.m. on May 30, 2022.Further, decomposed field data 252 may include elements of customer data254, payment data 256, transaction data 258, counterparty data 260, andremittance information 262 that collectively identify and characterizethe request, from the second retailer (e.g., “Woody's Suits”) on May 30,2022, for the real-time payment of $75.00 from user 101 for the purchaseof the pants initiated at 11:15 a.m. on May 30, 2022. As describedherein, decomposed field data 266 may include elements of customer data268, payment data 270, transaction data 272, counterparty data 274, andremittance information 276 that collectively identify and characterizethe request, from the third retailer (e.g., “Claude's Clothes”) on May30, 2022, for the real-time payment of $175.00 from user 101 for thepurchase of the jacket initiated at 12:30 p.m. on May 30, 2022.

In some instances, and based on portions of decomposed field data 204,252, and 266, executed notification engine 150 may perform operationsthat generate payment notifications 324, 326, and 328 associated with,respectively, the $50.00 real-time payment requested by the firstretailer, the $75.00 real-time payment requested by the second retailer,and the $175.00 real-time payment requested by the third retailer, andthat package each of payment notifications 324, 326, and 328 into acorresponding portion of notification data 330. For example, and for the$50.00 real-time payment requested by the first retailer, executednotification engine 150 may parse payment data 208 to obtain paymentinformation 332 that identifies the requested payment date of May 30,2022 and the payment instrument selected by user 101 to fund thepurchase transaction (e.g., the account number “XXXX-1234-5678-9012” ofthe deposit account held by user 101). Executed notification engine 150may also parse transaction data 210 to obtain information 334 thatidentifies the requested $50.00 payment amount and payment currency ofUS dollars, and may parse counterparty data 212 to obtain a name 336 ofthe first retailer (e.g., a name of the first retailer “Sam'sHaberdashery”).

In some examples, executed notification engine 150 may performoperations that package all, or selected portion of, information 332,information 334, and merchant name 336 into corresponding portions ofpayment notification 324, which may be incorporated within notificationdata 330. Further, although not illustrated in FIG. 3B, executednotification engine 150 may perform similar operations to generatepayment notification 326 based on corresponding portions of customerdata 254, payment data 256, transaction data 258, counterparty data 260,and remittance information 262 maintained within the elements ofdecomposed field data 252, and to generate payment notification 328based on corresponding portions of customer data 268, payment data 270,transaction data 272, counterparty data 274, and remittance information276 maintained within the elements of decomposed field data 266.

Further, executed notification engine 150 may parse pre-approved productdata 322 and obtain product identifier 316 of the pre-approved,unsecured installment loan (e.g., a product name, etc.) and the elementsof pre-approval data 320, which identify the loan amount (e.g., $375.00)and the pre-approved terms and conditions (e.g., the fixed, 6.0%interest rate, the five-month term, and the $76.00 monthly payment), andexecuted notification engine 150 may package all, or a subset of,product identifier 316 and the elements of pre-approved product data 322(e.g., the $375.00 loan amount, and the pre-approved terms andconditions) into a product notification 338. Further, executednotification engine 150 may access incentive data store 144, identifyone or more data records, such as data record 340, that includes orreferences product identifier 316. In some instances, data record 340may include one or more elements of digital content 342 that, whenrendered for presentation within a digital interface by client device102, offers the pre-approved, unsecured installment loan to user 101 inaccordance with the terms and conditions, and prompts user 101 toaccept, or alternatively, decline, the offer to fund not only the$300.00 real-time payments requested by “Sam's Haberdashery,” “Woody'sSuits,” and “Claude's Clothes” during the prior, two-hour temporalinterval, but also the predicted additional, $75.00 purchase of shoesfrom the retailer in the Georgetown neighborhood of Washington, D.C.,using the pre-approved, unsecured installment loan subject to thedetermined terms and conditions (e.g., consistent with user 101's intentto purchase apparel in the Georgetown neighborhood of Washington, D.C.to support user 101's transition to the new position). Executednotification engine 150 may obtain and package the elements of digitalcontent 342 into product notification 338, which executed notificationengine 150 may package into a corresponding portion of notification data330. Executed notification engine 150 may perform additional operationsthat cause FI computing system 130 to transmit notification data 330across network 120 to client device 102.

A programmatic interface associated with one or more applicationprograms executed at client device 102, such as an applicationprogramming interface (API) 344 associated with mobile bankingapplication 108, may receive notification data 330 and performoperations that cause client device 102 to executed mobile bankingapplication 108 (e.g., through a generation of a programmatic command,etc.). Upon execution by the one or more processors of client device102, executed mobile banking application 108 may receive notificationdata 330 from API 344, and a extraction module 346 of executed mobilebanking application 108 may parse notification data 330 to obtain one ormore of payment notifications 324, 326, and 328 and product notification338, which includes product identifier 316, pre-approval data 320 (e.g.,identifying the pre-approved, unsecured installment loan, the $375.00loan amount, and the pre-approved terms and conditions) and the elementsof digital content 342.

In some instances, extraction module 346 may provide productnotification 338 as input to an interface element generation module 348of executed mobile banking application 108, which may perform operationsthat generate and route interface elements 350 to display unit 109A. Insome instances, when rendered for presentation within a correspondingnotification interface 352 by display unit 109A, interface elements 350provide a graphical representation 354 to user 101 of productnotification 338, which identifies the availability of the $375.00unsecured installment loan to fund the $300.00 in real-time paymentsrequested by “Sam's Haberdashery,” “Woody's Suits,” and “Claude'sClothes” on May 30, 2022 for the first, second, and third purchasetransactions initiated by user 101 during the prior, two-hour temporalinterval, and the predicted occurrence of the $75.00 purchasetransaction for shoes during the future temporal interval, in accordancewith the pre-approved terms and conditions, e.g., within a singledisplay screen or window, or across multiple display screens or windows,of notification interface 352. Further, interface elements 350 may, whenpresented within notification interface 352, prompt user 101 to accept,or reject, the available, unsecured installment loan by providingfurther input to a respective one of an “ACCEPT” icon 356 and a“DECLINE” icon 358 presented within notification interface 352.

In some instances, user 101 may elect to accept the offer of theavailable, unsecured installment loan in accordance with thepre-approved terms and conditions (e.g., the $375.00 loan amount, thefixed, 6.0% interest rate, the five-month term, and the $76.00 monthlypayments) and to fund the $300.00 in real-time payments requested by“Sam's Haberdashery,” “Woody's Suits,” and “Claude's Clothes” on May 30,2022 using funds provided by the pre-approved, unsecured installmentloan. User 101 may provide input to client device 102 (e.g., via inputunit 109B) that selects the “ACCEPT” icon 356. Based on the input,executed mobile banking application 108 may perform operations (notillustrated in FIG. 3B), that generate and transmit a response tonotification data 330 that includes a product confirmation indicative ofuser 101's acceptance of the available, unsecured installment loan inaccordance with the terms and conditions across network 120 to FIcomputing system 130.

In some instances, also not illustrated in FIG. 3B, FI computing system130 may receive the response, and based on the confirmation data,perform operations that access pre-approved product data 322 (e.g., asmaintained within data repository 134), and that, in real-time andcontemporaneously with the initiation of each of the first, second, andthird purchase transaction, complete the internal qualification orunderwriting processes for the accepted, unsecured installment loan andissue the unsecured installment loan to user 101 in accordance with thedetermined terms and conditions, debit the $300.00 from an accountassociated with the issued, unsecured installment loan, and credit,respectively, the $50.00 to the account associated with the firstretailer (e.g., “Sam's Haberdashery”) and specified within payment data208 of decomposed field data 204, the $75.00 to the account associatedwith the second retailer (e.g., “Woody's Suits”) and specified withinpayment data 256 of decomposed field data 252, and the $175.00 to theaccount associated with the third retailer (e.g., “Claude's Clothes”)and specified within payment data 270 of decomposed field data 266(e.g., either directly, if the financial institution issues the accountassociated with the first retailer, the second retailer, or the thirdretailer, or based on additional ISO-20022-compliant RTP messagesexchanged with computing systems associated with other financialinstitutions). FI computing system 130 may also perform operations thataccess RFP messages 202A, 202B, and 202C maintained within RFP queue 135(along with corresponding elements of decomposed field data 204, 252,and 266), and delete RFP messages 202A, 202B, and 202C, and the elementsof decomposed field data 204, 252, and 266, from RFP queue 135, e.g.,based on the acceptance of the pre-approved, unsecured installment loanby user 101.

Further, FI computing system 130 may also perform operations thattransmit one or more messages to merchant systems 110, 114, and 118 thatconfirm the real-time clearance and settlement of the payments requestedby respective ones of the first retailer, the second retailer, or thethird retailer, either directly across network 120 or through one ormore of computing systems or devices associated with participants in theRTP ecosystem (e.g., additional ISO-20022-compliant messages, etc.).Based on the one or more messages, merchant systems 110, 114, and 118may perform operations that enable the first retailer, the secondretailer, and the third retailer to execute corresponding ones of theinitiated purchase transaction and provision the purchased products orservices, to user 101.

In other instances, user 101 may elect to decline the offer of theunsecured installment loan in accordance with the pre-approved terms andconditions, and user 101 may provide additional input to client device102 (e.g., via input unit 109B) that selects the “DECLINE” icon 358.Referring to FIG. 3C, and based on the additional input, executedextraction module 346 may route payment notifications 324, 326, and 328to executed interface element generation module 348, which may performoperations that generate and route corresponding interface elements 362,364, and 366 to display unit 109A. For example, when presented withinnotification interface 352 by display unit 109A, interface elements 362may provide a graphical representation 368 of payment notification 324that prompts user 101 to approve or reject the $50.00 payment requestedby the first retailer (e.g., “Sam's Haberdashery”) for the purchasedshirt on May 30, 2022, e.g., based on additional input provided to inputunit 109B of client device 102 that selects a respective one of an“APPROVE” icon 370 and a “REJECT” icon 372.

Further, when presented within notification interface 352 by displayunit 109A, interface elements 364 may provide a graphical representation374 of payment notification 326 that prompts user 101 to approve orreject the $75.00 payment requested by the second retailer (e.g.,“Woody's Suits”) for the purchased pants on May 30, 2022, e.g., based onadditional input provided to input unit 109B of client device 102 thatselects a respective one of an “APPROVE” icon 378 and a “REJECT” icon380. Additionally, when presented within notification interface 352 bydisplay unit 109A, interface elements 366 may provide a graphicalrepresentation 382 of payment notification 328 that prompts user 101 toapprove or reject the $175.00 payment requested by the third retailer(e.g., “Claude's Clothes”) for the purchased jacket on May 30, 2022,e.g., based on additional input provided to input unit 109B of clientdevice 102 that selects a respective one of an “APPROVE” icon 384 and a“REJECT” icon 386.

User 101 may, for example, elect to approve one or more of the $50.00payment requested by first retailer (e.g., “Sam's Haberdashery”), the$75.00 payment requested by the second retailer (e.g., “Woody's Suits”),or the $175.00 payment requested by the third retailer (e.g., “Claude'sClothes”), and user 101 may provide input to client device 102 (e.g.,via input unit 109B) that selects a corresponding one of “APPROVE” icon370, 378, or 374. Based on the input, executed mobile bankingapplication 108 may perform operations (not illustrated in FIG. 4B),that generate and transmit an additional response to notification data330 that include a payment confirmation indicative of each of the one ormore approved payments across network 120 to FI computing system 130,which may perform operations that, in real-time, debit an amount offunds consistent with each of the approved payments (e.g., the $50.00,$75.00, and/or $175.00 payment) from an account held by user 101 andassociated with the selected payment instrument, and that credit theamount of funds to an account associated with a corresponding one of thefirst retailer, the second retailer, or the third retailer, eitherdirectly across network 120, as specified with a corresponding one ofRFP messages 202A, 202B, or 202C (e.g., either directly, if thefinancial institution issues the account of the corresponding one of thefirst retailer, the second retailer, or the third retailer, or based onadditional ISO-20022-compliant RTP messages exchanged with computingsystems associated with other financial institution). FI computingsystem 130 may also perform operations that access RFP messages 202A,202B, and 202C maintained within RFP queue 135 (along with correspondingelements of decomposed field data 204, 252, and 266), and delete one ormore of RFP messages 202A, 202B, and 202C, and the elements ofdecomposed field data 204, 252, and 266, that are associated with theapproved, real-time payment (or payments) from RFP queue 135.

Further, FI computing system 130 may also perform operations thattransmit one or more messages to one or more of merchant systems 110,114, and 118 associated with the approved, real-time payments, e.g., toconfirm the real-time clearance and settlement of the approved,real-time payments requested by respective ones of the first retailer,the second retailer, or the third retailer, either directly acrossnetwork 120 or through one or more of computing systems or devicesassociated with participants in the RTP ecosystem (e.g., additionalISO-20022-compliant messages, etc.). Based on the one or more messages,merchant systems 110, 114, or 118 may perform operations that enable thefirst retailer, the second retailer, or the third retailer to executecorresponding ones of the initiated purchase transactions associatedwith the approved payments and provision the purchased products orservices, to user 101.

In other instances, and based on confirmation data indicating arejection by user 101 of one or more of the requested real-time payments(e.g., based on additional input selecting corresponding ones of“REJECT” icon 372, 380, and 386), FI computing system 130 may performoperations that access RFP messages 202A, 202B, and 202C maintainedwithin RFP queue 135 (along with corresponding elements of decomposedfield data 204, 252, and 266), and delete from RFP queue 135 one or moreof RFP messages 202A, 202B, and 202C, and the elements of decomposedfield data 204, 252, and 266, that are associated with the rejected,real-time payment (or payments), and generate and transmit one or moremessages to corresponding ones of merchant systems 110, 114, and 118indicative of the rejected payment, either directly across network 120or through one or more of computing systems or devices associated withparticipants in the RTP ecosystem (e.g., additional ISO-20022-compliantmessages, etc.). Based on the indication of the rejection of therequested payment by user 101 (e.g., due to potential fraud, etc.),corresponding ones of merchant systems 110, 114, and 118 may performoperations that enable the first retailer, the second retailer, or thethird retailer, respectively, to cancel the initiated purchasetransaction in real-time and without delays and chargebackscharacteristic of the transaction reconciliation, clearance, andsettlement processes involving payment rails and transaction-processingmessages.

FIGS. 4A, 4B, and 4C are flowcharts of exemplary processes forprovisioning directed product data to customer devices based onstructured messaging data, in accordance with some exemplaryembodiments. For example, one or more computing systems associated witha financial institution, such as FI computing system 130, may performone or more of the steps of exemplary process 400, as described below inreference to FIG. 4A, and one or more of the steps of exemplary process460, as described below in reference to FIG. 4C. Further, a computingdevice associated with, or operable by, user 101, such as client device102, may perform one or more of the steps of exemplary process 430, asdescribed below in reference to FIG. 4B.

Referring to FIG. 4A, FI computing system 130 may perform any of theexemplary processes described herein to receive a plurality ofrequest-for-payment (RFP) messages having message fields structured inaccordance with the ISO 20022 standard (e.g., in step 402 of FIG. 4A),and to store the received RFP message within a message queue (e.g., instep 404 of FIG. 4A). As described herein, each of the RFP messages maybe associated with a corresponding exchange of data, such as a purchasetransaction, initiated between a first counterparty (e.g., a customer ofthe financial institution, such as user 101) and a corresponding secondcounterparty (e.g., one of the first, second, and third retailersassociated corresponding ones of merchant systems 110, 114, and 118),and the message fields of each of the RFP messages may include data thatidentifies and characterizes a real-time payment requested from thefirst counterparty be the corresponding second counterparty, e.g., tofund a corresponding one of the initiated purchase transactions.Further, in some instances, the first counterparty may initiate each ofthe purchase transactions during a corresponding temporal interval,e.g., a prior temporal interval that precedes the receipt of theplurality of RFP messages by FI computing system 130.

In some instances, FI computing system 130 may receive each of the RFPmessages from a computing system of the corresponding secondcounterparty (e.g., a corresponding one of merchant systems 110, 114,and 118 of FIG. 1 ), or from one or more intermediate computing systemassociated with the RTP ecosystem, such as, but not limited to, acomputing system of a clearinghouse. Further, as described herein, eachof the RFP messages may be structured in accordance with the ISO 20022standard for electronic data exchange between financial institutions,and in some examples, each of the RFP messages may correspond to apain.013 message as specified within the ISO 20022 standard.

FI computing system 130 may also perform operations, described herein,to access mapping data that identifies and characterizes each of themessages fields within the ISO-20022-compliant RFP messages (e.g., instep 406 of FIG. 4A). Based on the mapping data, FI computing system 130may perform any of the exemplary processes described herein to parse andanalyze all or a selected subset of the message fields of each of theRFP messages to extract, obtain, or derive corresponding, discreteelements of decomposed field data that identify and characterize thefirst counterparty (e.g., user 101, the corresponding secondcounterparty (e.g., one of the merchants associated with merchantsystems 110, 114, or 118), the initiated purchase transaction, and therequested, real-time payment (e.g., in step 408 of FIG. 4A). FIcomputing system 130 may perform additional operations, describedherein, that store the extracted, obtained, or derived data elements ofdecomposed field data associated with each of the RFP messages, withinan accessible data repository, e.g., within an element of RTP data store144 of FIG. 1 .

In other instances, also in step 408, FI computing system 130 mayperform any to the exemplary processes described herein to detect,within one or more of the message fields of a corresponding one of theRFP messages, a link to the remittance data associated with therequested, real-time payment or the corresponding initiated purchase(e.g., a link to a PDF or HTML invoice or receipt associated with thecorresponding initiated purchase transaction). By way of example, thelink may correspond to a long-form uniform resource locator (URL) intowhich certain elements of data may be embedded, such as, but not limitedto, a unique identifier of the customer, and FI computing system 130 mayperform operations, described herein, that parse the long URL toidentify and extract the embedded data, which FI computing system 130may store within one or more of the discrete elements of decomposedfield data associated with the corresponding one of the RFP messages.

Additionally, or alternatively, in step 408, FI computing system 130 mayperform any of the exemplary processes described herein that, based onthe detected link (e.g., the long-form URL described above, or ashortened URL, such as a tiny URL), programmatically access theremittance data associated with the processed link, e.g., as maintainedat a corresponding one of merchant system 110, 114, or 118. Theremittance data may include a PDF or HTML invoice or bill (e.g.,formatted invoice data 218 of FIG. 2A), and the FI computing system 130may perform operations that process the remittance data (e.g., throughan application of an optical character recognition (OCR) process to thePDF invoice or bill, parsing code associated with the HTML invoice orbill, applying a screen-scraping technology to the invoice or bill,etc.) to extract the additional or alternate elements of the data thatidentifies and characterizes user 101, the second counterparty, theinitiated purchase transaction, and the requested, real-time payment.For instance, and based on the processed remittance data, the FIcomputing system may obtain elements of transaction that, among otherthings, identifies a product or service involved in the initiatedpurchase transaction (e.g., a UPC, etc.), a transaction time or date, orgranular data characterizing the initiated purchase transaction, such asa transaction subtotal and an amount of local sales tax. As describedherein, FI computing system 130 may store these additional, obtainedelements of transaction within one or more of the discrete elements ofdecomposed field data associated with the corresponding one of the RFPmessages.

In some instances, FI computing system 130 may also perform any of theexemplary processes described herein to process the elements ofdecomposed field data associated with each of the RFP messages, andobtain information that characterizes the each of the secondcounterparties (e.g., the retailers) involved in the purchasetransactions initiated by the customer during the prior temporalinterval, and values of parameters that characterize each of theinitiated purchase transactions during the prior temporal interval(e.g., in step 410 of FIG. 4A). The obtained counterparty information,for example, include a counterparty identifier of each of thecounterparties (e.g., a counterparty name, a standard industrialclassification (SIC) code, etc.) and geographic data characterizing eachof the counterparties (e.g., a portion of a corresponding postaladdress, etc.), and parameter values may include, but are not limited, atransaction date or time, a transaction value, and identifiers of one ormore products or services involved in corresponding ones of the first,second, and third purchase transactions.

FI computing system 130 may also perform any of the exemplary processesdescribed herein to identify a customer peer group of additionalcustomer of the financial institution that are demographically similarto the customer (e.g., additional customers that are characterized by avalue of one or more demographic parameters that are similar to acorresponding parameter value that characterizes the customer), and toobtain one or more elements of peer interaction data associated witheach of the additional customers during a current temporal intervaland/or across one or more temporal intervals (e.g., in step 412 of FIG.4A). As described herein, the elements of peer interaction dataassociated with a corresponding one of the additional customers mayinclude, among other things, one or more elements of account data 140B,transaction data 140C, and third-party data 140D (e.g., elements ofcredit-bureau data, etc.) associated with the corresponding one of theadditional customers during the current temporal interval and/or acrossthe one or more temporal intervals. Further, by way of example, thedemographic parameters may include, among other things, a customer ageor a city of residence of customer, and a value of one of thedemographic parameters that characterizes one of the additionalcustomers may be similar to a corresponding parameter value thatcharacterizes the customer when the parameter value is equivalent to thecorresponding parameter value, when parameter value falls within apredetermined, threshold amount of the corresponding parameter value, orwhen the parameter value is disposed within a specified value range thatincludes the corresponding parameter value.

Based on portions of the obtained counterparty information, the obtainedtransaction parameter values, and the customer-specific elements of peerinteraction data associated with one or more of the additionalcustomers, FI computing system 130 may perform any of the exemplaryprocesses described herein to predict an occurrence of one or moreadditional purchases transactions during a future temporal interval thatwould be consistent with the spending or transactional behavioral of thecustomer during the prior temporal interval, and that generate elementsof predicted output data that include a value of one or more parametersthat characterize each of the predicted occurrences of the additionalpurchases transactions during the future temporal interval, and in someinstances, an aggregate transaction amount across the prior and futuretemporal intervals (e.g., in step 414 of FIG. 4A). By way of example,the transaction parameter values that characterize each of the predictedoccurrences of the additional purchase transactions during the futuretemporal interval, may include, but are not limited to, a transactionamount, an identifier of a corresponding product or service, atransaction location (e.g., a postal code, etc.), and/or an identifierof a corresponding counterparty, such as an additional retailer.

Further, in some instances, FI computing system 130 may performoperations that generate the elements of predicted output data based onan application of a trained machine learning or artificial intelligenceprocess to elements (e.g., “feature values”) of an input datasetobtained, or extracted from, the obtained counterparty information, theobtained transaction parameter values, and/or the customer-specificelements of peer interaction data. As described herein, examples of thetrained machine-learning and artificial-intelligence processes mayinclude, but are not limited to, a clustering process, an unsupervisedlearning process (e.g., a k-means algorithm, a mixture model, ahierarchical clustering algorithm, etc.), a semi-supervised learningprocess, a supervised learning process, or a statistical process (e.g.,a multinomial logistic regression model, etc.). The trainedmachine-learning and artificial-intelligence processes may also include,among other things, a decision tree process (e.g., a boosted decisiontree algorithm, etc.), a random decision forest, an artificial neuralnetwork, a deep neural network, or an association-rule process (e.g., anApriori algorithm, an Eclat algorithm, or an FP-growth algorithm).Further, and as described herein, each of these exemplarymachine-learning and artificial-intelligence processes may be trainedagainst, and adaptively improved using, elements of training andvalidation data, and may be deemed successfully trained and ready fordeployment when a value of one or more performance or accuracy metricsare consistent with one or more threshold training or validationcriteria.

Referring back to FIG. 4A, FI computing system 130 may perform any ofthe exemplary processes described herein to obtain informationidentifying a candidate credit or loan product that is available forprovisioning to user 101 and, if provisioned, would be appropriate tofund not only the purchase transactions initiated during the priortemporal interval associated with the queued RFP messages, but also thepredicted occurrences of the additional purchase transactions during thefuture temporal interval (e.g., in step 416 of FIG. 4A). Examples ofthese candidate credit or loan products include, but are not limited to,an unsecured personal loan or installment loan having a fixed orvariable interest rate, a line-of-credit secured against other customerasserts, or other secured or unsecured credit products, and the obtainedinformation may, for the candidate credit or loan product, include oneor more internal qualification or underwriting criteria that establishan availability of the candidate credit or loan product to user 101 andenable the financial institution to establish customer- orproduct-specific terms and conditions.

Further, FI computing system 130 may perform any of the exemplaryprocesses described herein to obtain one or more elements ofcredit-bureau data associated with the customer, such as, but notlimited to, a credit score or a number of credit inquiries during acurrent or prior temporal interval (e.g., in step 418 of FIG. 4A). FIcomputing system 130 may also perform any of the exemplary processesdescribed herein to obtain one or more elements of interaction data thatcharacterize interactions between the customer and financial productsissued by the financial institution across one or more temporalintervals, and one or more elements of comparative data that identify,characterize, and compare seasonal or year-over-year variations in aspending and transactional habit of the customer (e.g., also in step 418of FIG. 4A). By way of example, the elements of interaction data mayinclude, among other things, outstanding balances associated with one ormore of the financial products (e.g., outstanding balances associatedwith one or more credit-card accounts or unsecured personal loans,etc.), a current status of one or more of the financial products (e.g.,no overdue balances, delinquent, default, etc.), and cashflow metricsassociated with one or more of the financial products (e.g., a netcashflow though a deposit account, etc.).

FI computing system 130 may also perform any of the exemplary processesdescribed herein to establish an availability of the candidate credit orloan product the customer and determine customer- or product-specificterms and conditions for the available credit or loan product based onan application of the one or more internal qualification or underwritingcriteria to the portions of the elements of predicted output data, theelements of credit-bureau data, the elements of interaction data, and/orthe elements of comparative data (e.g., in step 420 of FIG. 4A). By wayof example, the available credit or loan product may include anunsecured installment loan, and the established customer- orproduct-specific terms and conditions for the unsecured installment loanmay include, but are not limited to, a loan amount, a fixed interestrate or a variable interest rate, a term, and a monthly payment.

In some instances, FI computing system 130 may perform any of theexemplary processes described herein to generate, for each of thereceived and queued RFP messages associated with purchase transactionsinitiated during the prior temporal interval, a payment notificationbased on corresponding elements of the decomposed field data extracted,obtained, or derived from a corresponding one of the received and queuedRFP messages (e.g., in step 422 of FIG. 4A). FI computing system 130 mayalso perform any of the exemplary processes described herein to generatea product notification associated with the available credit or loanproduct that is capable of funding the initiated purchase transactionsassociated with the RFP messages, and the predicted future occurrencesof the additional purchase transactions, in accordance with thedetermined set of “pre-approved” terms and conditions (e.g., in step 424of FIG. 4A). By way of example, the generated product notification mayinclude an identifier of the available unsecured installment loan, thecorresponding loan amount, and the one or more of the determined set of“pre-approved” terms and conditions. Further, in some instances, theproduct notification may also include one or more elements of digitalcontent that, when rendered for presentation within a digital interfaceby client device 102, offers the available credit or loan product to thecustomer and prompts the customer to accept, or alternatively, decline,the offer to fund not only the initiated purchase transactionsassociated with the RFP messages, but also the predicted futureoccurrences of the additional purchase transactions that would beconsistent with the determine customer intent or purpose of thepreviously initiated purchase transactions.

FI computing system 130 may perform operations, described herein, topackage the product notification and each of the payment notificationsinto corresponding elements of notification data, and that transmit theelements of notification data across network 120 to a computing deviceor system associated with the customer, such as client device 102 (e.g.,in step 426 of FIG. 4A). Exemplary process 400 may then be complete instep 428.

Referring to FIG. 4B, client device 102 may perform any of the exemplaryprocesses described herein to receive the elements of notification datafrom FI computing system 130, and store the elements of notificationdata within a portion of a tangible, non-transitory memory accessible toclient device 102 (e.g., in step 432 of FIG. 4B). Client device 102 mayalso perform any of the exemplary processes described herein to obtainthe product notification from the received elements of notificationdata, and generate, and render for presentation within a correspondingdigital interface, a graphical representation of portions of the productnotification (e.g., in step 434 of FIG. 4B). As described herein, thepresented graphical representation may prompt user 101 to accept, oralternatively, decline, the offer to fund not the purchase transactionsinitiated during a prior temporal interval (e.g., as associated with theRFP messages), but also the predicted future occurrences of theadditional purchase transactions that would be consistent with thedetermine customer intent or purpose of the previously initiatedpurchase transactions, using the pre-approved credit or loan product inaccordance with the determined terms and conditions.

Further, client device 102 may also receive, via input unit 109B,elements of user input indicative of an acceptance, or alternatively, adecision to decline, the offer to fund the previously initiated purchasetransactions and the predicted future occurrences of the additionalpurchase transactions using the pre-approved credit or loan product(e.g., in step 436 of FIG. 4B), and based on the elements of user input,client device 102 may determine whether the customer accepted, ordeclined, the offer (e.g., in step 438 of FIG. 4B). If, for example,client device 102 were to determine that user 101 accepted the offer tofund the previously initiated purchase transactions and the predictedfuture occurrences of the additional purchase transactions using thepre-approved credit or loan product (e.g., step 438; YES), client device102 may perform any of the exemplary processes described herein toprocess the elements of input data and generate a product confirmationindicative of the acceptance, by user 101, of the offer to fund thepreviously initiated purchase transactions and the predicted futureoccurrences of the additional purchase transactions using thepre-approved credit or loan product, and to transmit a response to thenotification data that includes the product confirmation to FI computingsystem 130 (e.g., in step 440 of FIG. 4B). Exemplary process 430 may becomplete in step 442.

Alternatively, if client device 102 were to determine that user 101declined the offer to fund the previously initiated purchasetransactions and the predicted future occurrences of the additionalpurchase transactions using the pre-approved credit or loan product(e.g., step 438; NO), client device 102 may perform any of the exemplaryprocesses described herein to generate an additional productconfirmation indicating the rejection of the offer by user 101 (e.g., instep 444 of FIG. 4B), and to obtain the each of the payment notificationfrom the received elements of notification data, and generate, andrender for presentation within a corresponding digital interface, agraphical representation of each of the payment notifications thatprompts user 101 to approve, or alternatively, reject, the real-timepayment requested from user 101 by corresponding ones of the firstretailer, the second retailer, and the third retailer (e.g., in step 446of FIG. 4B). Client device 102 may receive, via input unit 109B,elements of user input indicative of an approval, or alternatively, arejection, of each of the requested, real-time payment by user 101(e.g., in step 448 of FIG. 4B), and based on the elements of user input,client device 102 may determine whether user 101 approved, or rejected,each of the requested real-time payment, and for each of the requestedreal-time payments, generate a payment confirmation indicative of theapproval or the rejection, by user 101, of the corresponding, requestedreal-time payment (e.g., in step 450 of FIG. 4B). Client device 102 mayperform any of the exemplary processes described herein to transmit aresponse to the notification data that includes the additional productconfirmation and each of the payment confirmations to FI computingsystem 130 (e.g., in step 452 of FIG. 4B). Exemplary process 430 maythen be complete in step 444.

Referring to FIG. 4C, FI computing system 130 may receive the elementsof response data from client device 102, and may store the receivedelements of response data within one or more tangible, non-transitorymemories accessible to FI computing system 130, such as in conjunctionwith the elements of decomposed field data within data repository 134(e.g., in step 462 of FIG. 4C). FI computing system 130 may also performany of the exemplary processes described herein to obtain, from theelements of response data, the product confirmation indicative of theacceptance, or alternatively, the rejection, the offer to fund thepreviously initiated purchase transactions and the predicted futureoccurrences of the additional purchase transactions using thepre-approved credit or loan product (e.g., in step 464 of FIG. 4C), andto process the product confirmation and determine whether user 101accepted, or declined, the offer for the pre-approved credit or loanproduct (e.g., in step 466 of FIG. 4C).

If, for example, FI computing system 130 were to determine that user 101accepted the offer to fund the previously initiated purchasetransactions and the predicted future occurrences of the additionalpurchase transactions using the pre-approved credit or loan product(e.g., step 466; YES), FI computing system 130 may perform any of theexemplary processes described herein to complete a qualification orunderwriting process and issue the pre-approved credit or loan productto the customer (e.g., in step 468 of FIG. 4C). FI computing system 130may perform any of the exemplary processes described herein, in step468, to execute the each of the real-time payment requested byrespective ones of the merchants (e.g., respective ones of the firstretailer, the second retailer, and the third retailer) based on theelements of decomposed field data associated with corresponding ones ofthe RFP messages (e.g., as maintained within RTP data store 144). By wayof example, and for each of the requested real-time payments, FIcomputing system 130 may access the corresponding elements of decomposedfield data, obtain a payment amount, an identifier of a customer and anidentifier of an account of the corresponding merchant, and perform anyof the exemplary processes described herein to debit the payment amountfrom an account associated with the issued credit or loan product, andcredit the payment amount to the merchant account (e.g., eitherdirectly, if the financial institution issues the account associatedwith the first retailer, the second retailer, or the third retailer, orbased on additional ISO-20022-compliant RTP messages exchanged withcomputing systems associated with other financial institutions). FIcomputing system 130 may also perform operations that access and deletecorresponding ones of the RFP messages maintained within RFP queue 135.

Further, FI computing system 130 may also perform operations in step 468that transmit one or more messages to a merchant computing systemassociated with each of the real-time payments funded using the issuedcredit or loan product (e.g., merchant systems 110, 114, and 118) thatconfirms the real-time clearance and settlement of the a correspondingone of the requested, real-time payments, either directly across network120 or through one or more of computing systems or devices associatedwith participants in the RTP ecosystem (e.g., additionalISO-20022-compliant messages, etc.). Based on the one or more messages,the merchant computing systems perform operations that enable themerchants to execute corresponding ones of the initiated purchasetransaction and provision the purchased products or services, to user101. Exemplary process 460 may then be complete in step 470.

Alternatively, if FI computing system 130 were to determine that user101 decline the offer to fund the requested, real-time payment using thepre-approved credit or loan product (e.g., step 466; NO), FI computingsystem 130 may perform any of the exemplary processes described hereinto obtain, from the elements of response data, one of the paymentconfirmations indicative of the approval, or alternatively, therejection, of a corresponding one of the real-time payment requests(e.g., in step 472 of FIG. 4C), and determine whether user 101 approved,or rejected, the corresponding one of the requested, real-time payments(e.g., in step 474 of FIG. 4C).

If, for example, FI computing system 130 were to determine that user 101approved the requested, real-time payment associated with the selectedpayment confirmation (e.g., step 474; YES), FI computing system 130 mayperform any of the exemplary processes described herein to execute thenow-approved real-time payment based on the payment confirmation and inaccordance with the corresponding elements of decomposed field data, andthat delete the RFP message from the RFP message queue (e.g., in step476 of FIG. 4C).

FI computing system 130 may parse the elements of response data anddetermine whether additional payment confirmations await processing(e.g., in step 478 of FIG. 4C). If FI computing system 130 were todetermine that additional payment confirmations await processing (e.g.,step 478; YES), exemplary process 460 may pass back to step 474, and FIcomputing system 130 may select an additional payment confirmation forprocessing. Alternatively, if FI computing system 130 were to determinethat no additional payment confirmations await processing (e.g., step478; NO), exemplary process 460 may then be complete in step 470.

Referring back to step 474, if FI computing system 130 were to determinethat user 101 rejected the requested, real-time payment (e.g., step 474;NO), FI computing system 130 may perform any of the exemplary processesdescribed herein to broadcast one or more additional ISO-20022-compliantRTP messages that confirm the rejection of the requested, real-timepayment by user 101, and that delete the RFP message from the RFP queue(e.g., in step 480 of FIG. 4C). Exemplary process 460 may be complete instep 470.

In other examples, and based on a temporal pendency within RFP queue 135of a particular one of the RFP massages associated with the requested,real-time payment subject to funding by the issued credit or loanproduct (e.g., the temporal pendency exceeds a predetermined, thresholdpendency), FI computing system 130 may perform any of the exemplaryprocesses described herein to generate and provision a correspondingpayment notification to client device 102, e.g., for presentation withina corresponding digital interface, such as notification interface 352 ofFIGS. 3A and 3B, prior to a performance of the exemplary processesdescribed herein that determine the customer intent or purpose of theinitiated purchase transactions associated with the queued RFP massagesthat predict occurrences of additional purchase transactions during afuture temporal interval that would be consistent with the determinedcustomer intent or purpose, and that pre-approve user 101 for acandidate credit or loan product capable of funding the initiatedpurchase transactions and the predicted future occurrences of theadditional purchase transactions during the future temporal interval.

By way of example, the presented payment notification may prompt user101 to fund the $50.00 real-time payment requested by the first retailer(e.g., “Sam's Haberdashery”) on May 30, 2022, for the shirt purchased at10:30 a.m. on May 30th (e.g., in step 444 of FIG. 4B). Based on thepresent payment notification, user 101 may provide input to clientdevice 102, via input unit 109B, elements of user input indicative of anapproval of the requested, $50.00 real-time payment (e.g., in step 446of FIG. 4B), and client device 102 may generate, and transmit acrossnetwork 120 to FI computing system 130, a payment confirmationindicative of the approval, by user 101, of the corresponding, requestedreal-time payment (e.g., in steps 448 and 450 of FIG. 4B). Upon receiptof the payment confirmation, FI computing system 130 may perform any ofthe exemplary processes described herein to execute the now-approvedreal-time payment of $50.00 to the first retailer (e.g., “Sam'sHaberdashery”) based on the payment confirmation and in accordance withthe corresponding elements of decomposed field data, e.g., by debiting$50.00 from the deposit account of user 101 specified within paymentdata 208 of decomposed field data 204, and by crediting the $50.00 tothe account of the first retailer specified within payment data 208 ofdecomposed field data 204 using any of the exemplary processes describedherein.

In some instances, user 101 may elect to the provisioned offer to fundthe previously initiated purchase transactions, which include the $50.00real-time payment requested by the first retailer on May 30, 2022, andthe predicted future occurrences of the additional purchase transactionsusing the pre-approved credit or loan product, subsequent to theapproval of the requested, $50.00 real-time payment. Based on the priorapproval of the requested, $50.00 real-time payment, FI computing system130 may perform operations, in step 468 of FIG. 4C, to reverse theexecution of the requested, $50.00 real-time payment using the fundsmaintained within the deposit account of user 101 by crediting the$50.00 to the deposit account user 101 and debiting the previouslycredited $50.00 from the account of the first retailer, and byperforming any of the exemplary processes described herein to debit the$50.00 payment amount from the account associated with the issued creditor loan product.

C. Exemplary Hardware and Software Implementations

Embodiments of the subject matter and the functional operationsdescribed in this disclosure can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisdisclosure, including decomposition engine 146, analytical engine 148,notification engine 150, application programming interfaces (APIs) 203and 344, remittance analysis engine 240, predictive module 302,pre-approval module 308, extraction module 346, and interface elementgeneration module 348, can be implemented as one or more computerprograms, i.e., one or more modules of computer program instructionsencoded on a tangible non-transitory program carrier for execution by,or to control the operation of, a data processing apparatus (or acomputing system). Additionally, or alternatively, the programinstructions can be encoded on an artificially-generated propagatedsignal, such as a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. The computer storage medium can be amachine-readable storage device, a machine-readable storage substrate, arandom or serial access memory device, or a combination of one or moreof them

The terms “apparatus,” “device,” and “system” refer to data processinghardware and encompass all kinds of apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus, device, orsystem can also be or further include special purpose logic circuitry,such as an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit). The apparatus, device, orsystem can optionally include, in addition to hardware, code thatcreates an execution environment for computer programs, such as codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program, which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data, such as one ormore scripts stored in a markup language document, in a single filededicated to the program in question, or in multiple coordinated files,such as files that store one or more modules, sub-programs, or portionsof code. A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, such as an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Computers suitable for the execution of a computer program include, byway of example, general or special purpose microprocessors or both, orany other kind of central processing unit. Generally, a centralprocessing unit will receive instructions and data from a read-onlymemory or a random-access memory or both. The essential elements of acomputer are a central processing unit for performing or executinginstructions and one or more memory devices for storing instructions anddata. Generally, a computer will also include, or be operatively coupledto receive data from or transfer data to, or both, one or more massstorage devices for storing data, such as magnetic, magneto-opticaldisks, or optical disks. However, a computer need not have such devices.Moreover, a computer can be embedded in another device, such as a mobiletelephone, a personal digital assistant (PDA), a mobile audio or videoplayer, a game console, a Global Positioning System (GPS) or an assistedGlobal Positioning System (AGPS) receiver, or a portable storage device,such as a universal serial bus (USB) flash drive, to name just a few.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, such as EPROM, EEPROM, and flash memory devices; magneticdisks, such as internal hard disks or removable disks; magneto-opticaldisks; and CD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, such as user 101, embodiments ofthe subject matter described in this specification can be implemented ona computer having a display device, such as a CRT (cathode ray tube) orLCD (liquid crystal display) monitor, for displaying information to theuser and a keyboard and a pointing device, such as a mouse or atrackball, by which the user can provide input to the computer. Otherkinds of devices can be used to provide for interaction with a user aswell; for example, feedback provided to the user can be any form ofsensory feedback, such as visual feedback, auditory feedback, or tactilefeedback; and input from the user can be received in any form, includingacoustic, speech, or tactile input. In addition, a computer can interactwith a user by sending documents to and receiving documents from adevice that is used by the user; for example, by sending web pages to aweb browser on a user's device in response to requests received from theweb browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, such as a data server, or that includes a middlewarecomponent, such as an application server, or that includes a front-endcomponent, such as a client computer having a graphical user interfaceor a Web browser through which a user can interact with animplementation of the subject matter described in this specification, orany combination of one or more such back-end, middleware, or front-endcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, such as a communicationnetwork. Examples of communication networks include a local area network(LAN) and a wide area network (WAN), such as the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data, such as an HTML page, to auser device, such as for purposes of displaying data to and receivinguser input from a user interacting with the user device, which acts as aclient. Data generated at the user device, such as a result of the userinteraction, can be received from the user device at the server.

While this specification includes many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular embodiments of the disclosure. Certain features that aredescribed in this specification in the context of separate embodimentsmay also be implemented in combination in a single embodiment.Conversely, various features that are described in the context of asingle embodiment may also be implemented in multiple embodimentsseparately or in any suitable sub-combination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination may in some cases be excised from the combination, and theclaimed combination may be directed to a sub-combination or variation ofa sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

In each instance where an HTML file is mentioned, other file types orformats may be substituted. For instance, an HTML file may be replacedby an XML, JSON, plain text, or other types of files. Moreover, where atable or hash table is mentioned, other data structures (such asspreadsheets, relational databases, or structured files) may be used.

Various embodiments have been described herein with reference to theaccompanying drawings. It will, however, be evident that variousmodifications and changes may be made thereto, and additionalembodiments may be implemented, without departing from the broader scopeof the disclosed embodiments as set forth in the claims that follow.

Further, unless otherwise specifically defined herein, all terms are tobe given their broadest possible interpretation including meaningsimplied from the specification as well as meanings understood by thoseskilled in the art and/or as defined in dictionaries, treatises, etc. Itis also noted that, as used in the specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless otherwise specified, and that the terms “comprises” and/or“comprising,” when used in this specification, specify the presence oraddition of one or more other features, aspects, steps, operations,elements, components, and/or groups thereof. Moreover, the terms“couple,” “coupled,” “operatively coupled,” “operatively connected,” andthe like should be broadly understood to refer to connecting devices orcomponents together either mechanically, electrically, wired,wirelessly, or otherwise, such that the connection allows the pertinentdevices or components to operate (e.g., communicate) with each other asintended by virtue of that relationship. In this disclosure, the use of“or” means “and/or” unless stated otherwise. Furthermore, the use of theterm “including,” as well as other forms such as “includes” and“included,” is not limiting. In addition, terms such as “element” or“component” encompass both elements and components comprising one unit,and elements and components that comprise more than one subunit, unlessspecifically stated otherwise. Additionally, the section headings usedherein are for organizational purposes only and are not to be construedas limiting the described subject matter.

The foregoing is provided for purposes of illustrating, explaining, anddescribing embodiments of this disclosure. Modifications and adaptationsto the embodiments will be apparent to those skilled in the art and maybe made without departing from the scope or spirit of the disclosure.

What is claimed is:
 1. An apparatus comprising: a communicationsinterface; a memory storing instructions; and at least one processorcoupled to the communications interface and to the memory, the at leastone processor being configured to execute the instructions to: receive aplurality of messages via the communications interface, each of themessages being associated with a real-time payment requested from afirst counterparty by a corresponding second counterparty, and each ofthe messages comprising elements of message data that are disposedwithin corresponding message fields, and that characterize a firstexchange of data initiated between the first counterparty and thecorresponding second counterparty during a first temporal interval;based on the elements of message data, perform operations that predictan occurrence of a second exchange of data that involves the firstcounterparty during a second temporal interval, the second temporalinterval being disposed subsequent to the first temporal interval; andgenerate product data characterizing a product that is available to thefirst counterparty and associated with the predicted occurrence of thesecond data exchange, and transmit, via the communications interface,notification data that includes the product data to a device operable bythe first counterparty, the notification data causing an applicationprogram executed at the device to present a portion of the product datawithin a digital interface.
 2. The apparatus of claim 1, wherein the atleast one processor is further configured to execute the instructionsto: obtain, from the memory, mapping data associated with the messagefields of the messages; for each of the messages, perform operationsthat obtain the elements of message data from corresponding ones of themessage fields based on the mapping data; and store the elements ofmessage data associated with each of the messages within the memory, theelements of message data comprising, for a corresponding one of themessages, a first identifier of the first counterparty, a secondidentifier of the corresponding second counterparty, and a parametervalue characterizing the corresponding first data exchange.
 3. Theapparatus of claim 2, wherein: each of the messages comprises arequest-for-payment message, the message fields of each of therequest-for-payment messages being structured in accordance with astandardized data-exchange protocol; and the mapping data compriseselements that identify corresponding ones of the elements of the messagedata and corresponding ones of the message fields.
 4. The apparatus ofclaim 2, wherein the at least one processor is further configured toexecute the instructions to obtain, based on the mapping data,information associated with the first data exchange from one or more ofthe message fields of a corresponding one of the messages, theinformation comprising a uniform resource locator associated withelements of formatted data maintained by a computing system.
 5. Theapparatus of claim 4, wherein the at least one processor is furtherconfigured to execute the instructions to: based on the uniform resourcelocator, perform operations that request and receive one or more of theelements of formatted data from the computing system via thecommunications interface; and process the one or more elements offormatted data, and obtain at least one of (i) an additional parametervalue characterizing the first data exchange associated with thecorresponding message from the processed elements of formatted data or(ii) one or more elements of contextual data that characterize the firstdata exchange associated with the corresponding message based on theprocessed elements of formatted data.
 6. The apparatus of claim 1,wherein the at least one processor is further configured to: apply atrained machine-learning or artificial-intelligence process to an inputdataset comprising one or more of the elements of message data; andbased on the application of the trained machine-learning orartificial-intelligence process to the input dataset, generate outputdata characterizing the predicted occurrence the second exchange of dataduring the second temporal interval.
 7. The apparatus of claim 6,wherein the output data comprises a value of at least one parameter thatcharacterizes the predicted occurrence of the second exchange of dataduring the second temporal interval.
 8. The apparatus of claim 7,wherein the at least one processor is further configured to execute theinstructions to: determine an aggregated value of the parametercharacterizing the first exchanges of data during the first temporalinterval; and determine that the product is available to the firstcounterparty based on the first and second aggregated parameter values.9. The apparatus of claim 1, wherein the at least one processor isfurther configured to: based on one or more of the elements of messagedata, determine a value of at least one parameter that characterizes thefirst counterparty, and obtain identifiers of a plurality of peercounterparties characterized by the at least one parameter value; basedon the identifiers, obtain elements of behavioral data associated withthe plurality of peer counterparties, the behavioral data characterizinga behavior of one or more of the peer counterparties during the firsttemporal interval; and based on the elements of message data and theelements of behavioral data, perform operations that predict anoccurrence of a second exchange of data that involves the firstcounterparty during the second temporal interval.
 10. The apparatus ofclaim 1, wherein: the product data comprises a product identifier andinformation specifying a term or condition of the product; thenotification data comprises an element of digital content associatedwith the product; and the notification data causes the executedapplication program to present at least the portion of the product dataand the element of digital content within the digital interface.
 11. Theapparatus of claim 1, wherein the at least one processor is furtherconfigured to transmit the notification data to the device via thecommunications interface prior to an execution of at least one of thefirst exchanges of data initiated between the first counterparty and thecorresponding second counterparty.
 12. A computer-implemented method,comprising: receiving a plurality of messages using at least oneprocessor, each of the messages being associated with a real-timepayment requested from a first counterparty by a corresponding secondcounterparty, and each of the messages comprising elements of messagedata that are disposed within corresponding message fields, and thatcharacterize a first exchange of data initiated between the firstcounterparty and the corresponding second counterparty during a firsttemporal interval; based on the elements of message data, performingoperations, using the at least one processor, that predict an occurrenceof a second exchange of data that involves the first counterparty duringa second temporal interval, the second temporal interval being disposedsubsequent to the first temporal interval; and generating, using the atleast one processor, product data characterizing a product that isavailable to the first counterparty and associated with the predictedoccurrence of the second data exchange, and transmitting, using the atleast one processor, notification data that includes the product data toa device operable by the first counterparty, the notification datacausing an application program executed at the device to present aportion of the product data within a digital interface.
 13. Thecomputer-implemented method of claim 12, further comprising: obtaining,using the at least one processor, mapping data associated with themessage fields of the messages from a data repository; for each of themessages, performing operations, using the at least one processor, thatobtain the elements of message data from corresponding ones of themessage fields based on the mapping data; and storing, using the atleast one processor, the elements of message data associated with eachof the messages within the data repository, the elements of message datacomprising, for a corresponding one of the messages, a first identifierof the first counterparty, a second identifier of the correspondingsecond counterparty, and a parameter value characterizing thecorresponding first data exchange.
 14. The computer-implemented methodof claim 13, wherein: each of the messages comprises arequest-for-payment message, the message fields of each of therequest-for-payment messages being structured in accordance with astandardized data-exchange protocol; and the mapping data compriseselements that identify corresponding ones of the elements of the messagedata and corresponding ones of the message fields.
 15. Thecomputer-implemented method of claim 13, further comprising: based onthe mapping data, obtaining, using the at least one processor,information associated with the first data exchange from one or more ofthe message fields of a corresponding one of the messages, theinformation comprising a uniform resource locator associated withelements of formatted data maintained by a computing system; based onthe uniform resource locator, performing operations, using the at leastone processor, that request and receive one or more of the elements offormatted data from the computing system; and using the at least oneprocessor, processing the one or more elements of formatted data, andobtaining at least one of (i) an additional parameter valuecharacterizing the first data exchange associated with the correspondingmessage from the processed elements of formatted data or (ii) one ormore elements of contextual data that characterize the first dataexchange associated with the corresponding message based on theprocessed elements of formatted data.
 16. The computer-implementedmethod of claim 12, further comprising: using the at least oneprocessor, applying a trained machine-learning orartificial-intelligence process to an input dataset comprising one ormore of the elements of message data; and based on the application ofthe trained machine-learning or artificial-intelligence process to theinput dataset, generating, using the at least one processor, output datacharacterizing the predicted occurrence the second exchange of dataduring the second temporal interval.
 17. The computer-implemented methodof claim 16, wherein the output data comprises a value of at least oneparameter that characterizes the predicted occurrence of the secondexchange of data during the second temporal interval.
 18. Thecomputer-implemented method of claim 17, further comprising:determining, using the at least one processor, an aggregated value ofthe parameter characterizing the first exchanges of data during thefirst temporal interval; and determining, using the at least oneprocessor, that the product is available to the first counterparty basedon the first and second aggregated parameter values.
 19. Thecomputer-implemented method of claim 12, wherein: thecomputer-implemented method further comprises: based on one or more ofthe elements of message data, and using the at least one processor,determining a value of at least one parameter that characterizes thefirst counterparty, and obtaining identifiers of a plurality of peercounterparties characterized by the at least one parameter value; andbased on the identifiers, obtaining, using the at least one processor,elements of behavioral data associated with the plurality of peercounterparties, the behavioral data characterizing a behavior of one ormore of the peer counterparties during the first temporal interval; andthe performing comprises performing operations that predict anoccurrence of the second exchange of data during the second temporalinterval based on the elements of message data and the elements ofbehavioral data.
 20. A tangible, non-transitory computer-readable mediumstoring instructions that, when executed by at least one processor,cause the at least one processor to perform a method, comprising:receiving a plurality of messages, each of the messages being associatedwith a real-time payment requested from a first counterparty by acorresponding second counterparty, and each of the messages comprisingelements of message data that are disposed within corresponding messagefields, and that characterize a first exchange of data initiated betweenthe first counterparty and the corresponding second counterparty duringa first temporal interval; based on the elements of message data,performing operations that predict an occurrence of a second exchange ofdata that involves the first counterparty during a second temporalinterval, the second temporal interval being disposed subsequent to thefirst temporal interval; and generating product data characterizing aproduct that is available to the first counterparty and associated withthe predicted occurrence of the second data exchange, and transmitting,using the at least one processor, notification data that includes theproduct data to a device operable by the first counterparty, thenotification data causing an application program executed at the deviceto present a portion of the product data within a digital interface.